Method and system to detect noise in cardiac arrhythmic patterns

ABSTRACT

Computer implemented methods and systems for detecting noise in cardiac activity are provided. The method and system obtain a far field cardiac activity (CA) data set that includes far field CA signals for a series of beats, overlay a segment of the CA signals with a noise search window, and identify turns in the segment of the CA signals. The method and system determine whether the turns exhibit a turn characteristic that exceed a turn characteristic threshold, declare the segment of the CA signals as a noise segment based on the determining operation, shift the noise search window to a next segment of the CA signal and repeat the identifying, determining and declaring operations; and modify the CA signals based on the declaring the noise segments.

REFERENCE TO RELATED APPLICATIONS

The present application is a continuation application of, and claimspriority to, U.S. application Ser. No. 15/973,384, Titled “METHOD ANDSYSTEM TO DETECT NOISE IN CARDIAC ARRHYTHMIC PATTERNS” which was filedon 7 May 2018, the complete subject matter of which is expresslyincorporated herein by reference in its entirety.

FIELD OF THE INVENTION

Embodiments herein relate generally to implantable medical devices, andmore particularly to detection and discrimination of noise withinarrhythmic patterns of interest.

RELATED APPLICATIONS

The following applications relate to and are expressly incorporatedherein by reference in their entireties (hereafter referred to as“Co-Pending Related Applications”):

U.S. patent application Ser. No. 15/973,126, filed 7 May 2018, titled“METHOD AND SYSTEM FOR SECOND PASS CONFIRMATION OF DETECTED CARDIACARRHYTHMIC PATTERNS” (now U.S. Pat. No. 10,729,346, issued 4 Aug. 2020),

U.S. patent application Ser. No. 15/973,107, filed 7 May 2018 titled“METHOD AND SYSTEM TO DETECT P-WAVES IN CARDIAC ARRHYTHMIC PATTERNS”(now U.S. Pat. No. 10,856,761, issued 8 Dec. 2020),

U.S. patent application Ser. No. 15/973,307, filed 7 May 2018, titled“METHOD AND SYSTEM TO DETECT PREMATURE VENTRICULAR CONTRACTIONS INCARDIAC ARRHYTHMIC PATTERNS” (now U.S. Pat. No. 10,874,322, issued 29Dec. 2020, and

U.S. patent application Ser. No. 15/973,351, filed 7 May 2018, titled“METHOD AND SYSTEM TO DETECT R-WAVES IN CARDIAC ARRHYTHMIC PATTERNS”(now U.S. Pat. No. 11,020,036, issued 1 Jun. 2021.

BACKGROUND OF THE INVENTION

Atrial fibrillation (AF) is a common and serious cardiac arrhythmia,affecting more than two million people in the United States alone.Clinically, atrial fibrillation involves an abnormality of electricalimpulse formation and conduction that originates in the atria. Atrialfibrillation is characterized by multiple swirling wavelets ofelectrical current spreading across the atria in a disorganized manner.The irregularity of electrical conduction throughout the atria createsirregular impulse propagation through the atrioventricular (AV) nodeinto the ventricle.

Impulse propagation through the AV node may be extremely rapid, leadingto reduced diastolic filling of the heart chambers and a correspondingreduction of the cardiac pumping action. Increased heart rate and lossof AV synchrony may also exacerbate any underlying heart problems, suchas heart failure, coronary blood flow, or other pulmonary disorders.Alternatively, impulse propagation through the AV node may be verylimited due to AV node refractoriness so that atrial fibrillation can besustained indefinitely, as the ventricles continue to drive circulation,albeit inefficiently.

Atrial Fibrillation (AF) monitoring systems have been developed for usein an ambulatory setting, which may be either external, such as a Holtermonitor, or internal, such as implantable cardiac monitors or “looprecorders”. These systems continually sense cardiac electrical signalsfrom a patient's heart, process the signals to detect arrhythmias andupon detection, record the electrical signals for subsequent review andanalysis.

More recently, interest has increased in providing improved implantablecardiac monitors. It has been proposed that implantable cardiac monitorsmay be used for diagnosis of re-current AF after AF ablation,cryptogenic stroke, and other arrhythmias. Further, there is an interestin improved management of arrhythmia episodes in connection withmedication usage, as well as monitoring AF in connection with periodicatrial cardioversion.

Algorithms used by existing monitoring systems for detecting AF areprimarily based on an irregularity of R-R intervals. However, thesealgorithms may provide false positive AF detections when AF did notnecessarily exist. As one example, certain AF detection algorithms maybe confused when a patient exhibits sinus rhythm with irregular R-Rintervals.

Further, existing AF detection algorithms may experience undue falsepositives in connection with frequent premature ventricular contraction(PVC). Existing AF algorithms may not exhibit sufficient positivepredictive value (PPV) of AF episode detection and duration (burden).

An opportunity remains to improve the accuracy of signal markers thatare sensed and utilized for generating accurate diagnostics and forcomputing short/long term trends in physiological signals leading toactionable insights and predictions. Although recent improvements havebeen made in implantable device hardware, filters, and sensingalgorithms, false detection of bradycardia and asystole episodes remainsa challenge due to small amplitude signals, premature ventricularcontraction (PVC) beats, sudden drops in signal amplitude, suboptimaldevice programming, and loss of contact between subcutaneous tissue andelectrodes. Improved sensing algorithm performance could lead to reducedunnecessary data transmission to remote clinicians, episode reviewburden, and potentially prolong implantable cardiac monitor (ICM)longevity.

SUMMARY

In accordance with embodiments herein, a computer implemented method isprovided for detecting noise in cardiac activity. The method, undercontrol of one or more processors configured with specific executableinstructions, obtains a far field cardiac activity (CA) data set thatincludes far field CA signals for a series of beats, overlays a segmentof the CA signals with a noise search window, identifies turns in thesegment of the CA signals, and determines whether the turns exhibit aturn characteristic that exceed a turn characteristic threshold. Themethod further declares the segment of the CA signals as a noise segmentbased on the determining operation, shifts the noise search window to anext segment of the CA signal and repeat the identifying, determiningand declaring operations, and modifies the CA signals based on thedeclaring the noise segments.

Optionally, the turn characteristic corresponds to turn amplitude andwherein the determining operation comprises analyzing the turn amplituderelative to a turn amplitude threshold. Optionally, the turncharacteristic corresponds to turn frequency and wherein the determiningoperation comprises analyzing the turn frequency relative to a turnfrequency threshold. Optionally, the method further comprises settingnoise flags based on relations between the turn amplitude and turnamplitude threshold and between the turn frequency and turn frequencythreshold. Optionally, the identifying the turn comprises identifyingchanges in a signal direction by calculating a first derivate of the CAsignals at incremental points along the CA signals and finding thepoints where a sign of the derivative changes, labeling the points asturns.

Optionally, the overlaying operation comprises defining the noise searchwindow to overlap a portion of CA signal that does not overlap with aQRS complex or a T-wave in the segment of the CA signals. Optionally,the method further comprises applying an arrhythmia detection process,based on RR interval variability, to the CA signals as modified.Optionally, the method further comprises declaring a segment of the CAsignals to represent a noisy segment, the modifying operation comprisingremoving the noisy segment to form noise corrected CA signals, theapplying operation applying the arrhythmia detection process to thenoise corrected CA signals. Optionally, the arrhythmia detection processis performed as a first pass detection process by an on-board R-Rinterval irregularity (ORI) process that analyzes the CA signals afterbeing modified based on the declaring the noisy segments. Optionally,the overlaying, identifying, determining, declaring, shifting, modifyingoperations are performed by firmware and hardware within an ICM or IMD.

In accordance with embodiments herein a system is provided for detectingnoise in cardiac activity. The system comprises memory to store specificexecutable instructions and one or more processors configured to executethe specific executable instructions for: obtaining a far field cardiacactivity (CA) data set that includes far field CA signals for a seriesof beats, overlaying a segment of the CA signals with a noise searchwindow, identifying turns in the segment of the CA signals, anddetermining whether the turns exhibit a turn characteristic that exceeda turn characteristic threshold. The processors are further configuredfor declaring the segment of the CA signals as a noise segment based onthe determining operation, shifting the noise search window to a nextsegment of the CA signal and repeat the identifying, determining anddeclaring operations, and modifying the CA signals based on thedeclaring the noise segments.

Optionally, the characteristic corresponds to turn amplitude and whereinthe determining operation comprises analyzing the turn amplituderelative to a turn amplitude threshold. Optionally, the turncharacteristic corresponds to turn frequency and wherein the determiningoperation comprises analyzing the turn frequency relative to a turnfrequency threshold. Optionally, the processor is further configured toset noise flags based on relations between a turn amplitude and turnamplitude threshold and between a turn frequency and turn frequencythreshold. Optionally, the processor is further configured to apply anarrhythmia detection process that is dependent on RR intervalvariability, wherein the overlaying operation comprises defining thenoise search window to overlap a portion of CA signal that does notoverlap with a QRS complex or a T-wave in the segment of the CA signals.Optionally, the processor is further configured to identify changes in asignal direction by calculating a first derivate of the CA signals atincremental points along the CA signals and find the points where a signof the derivative changes, labeling the points as turns. Optionally, thesystem further comprises an implantable cardiac monitor that houses thememory and one or more processors, and that houses sensors to obtain theCA signals for the series of beats. Optionally, the system furthercomprises an implantable cardiac monitor that comprises sensors toobtain the CA signals and a telemetry circuit to telemeter the CAsignals to a local external device. Optionally, the system furthercomprises a local external device that includes the memory and one ormore processors for performing at least a portion of the overlaying,identifying, determining, declaring, shifting, and modifying operations.Optionally, the system further comprising a remote server that includesthe memory and one or more processors for performing at least a portionof the overlaying, identifying, determining, declaring, shifting, andmodifying operations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an implantable cardiac monitoring device (ICM)intended for subcutaneous implantation at a site near the heart inaccordance with embodiments herein.

FIG. 2A shows a block diagram of the ICM formed in accordance withembodiments herein.

FIG. 2B illustrates an automatic sensing control adjustment utilized bythe ORI process of the ICM in accordance with embodiments herein.

FIG. 2C illustrates cardiac activity data generated and stored by an ICMin accordance with embodiments herein.

FIG. 2D illustrates screenshots of displays in which episode statisticsand arrhythmia diagnostics may be presented to a physician in accordancewith an embodiment herein.

FIG. 2E illustrates screenshots of displays in which episode statisticsand arrhythmia diagnostics may be presented to a physician in accordancewith an embodiment herein.

FIG. 3 shows a high-level workflow for an enhanced confirmatory AFdetection process implemented in accordance with embodiments herein.

FIG. 4 illustrates a flow chart for classifying AF detection anddeveloping recommendations for sensitivity profile parameter settings inaccordance with embodiments herein.

FIG. 5 illustrates a system level diagram indicating potential devicesand networks in which the methods and systems herein may be utilized inaccordance with embodiments herein.

FIG. 6 illustrates a distributed processing system in accordance withembodiments herein.

FIG. 7 illustrates a collection of communications between the ICM, alocal device, a remote device and a server/database in accordance withembodiments herein.

FIG. 8A illustrates a block diagram of parallel signal processing pathsimplemented in accordance with embodiments herein.

FIG. 8B illustrates a portion of rectified CA signal processed by thesecond pass detection/confirmation algorithm of FIG. 8A in accordancewith embodiments herein.

FIG. 8C illustrates a portion of a CA data set that is analyzed inconnection with the operation at to identify amplitudes of features ofinterest in accordance with embodiments herein.

FIG. 9A illustrates a process for detecting bradycardia and asystoleepisodes implements by the second pass detection algorithm in accordancewith embodiments herein.

FIG. 9B illustrates an example process for detecting unanalyzable beatsegments due to noise in accordance with an embodiment herein.

FIG. 9C illustrates a process for confirming or denying a devicedocumented bradycardia or asystole episode in accordance withembodiments herein.

FIG. 9D illustrates a process for adaptively adjusting the sensitivityprofile parameters beat by beat (or ensemble by ensemble) based onR-wave and T-wave characteristics of interest in accordance withembodiments herein.

FIG. 9E illustrates a process for R-wave detection in accordance withembodiments herein.

FIG. 10A illustrates an example for identifying a T-wave peak amplitudein accordance with embodiments herein.

FIG. 10B illustrates examples of signals produced at the various featureenhancement operations within FIG. 9A in accordance with embodimentsherein.

FIG. 10C illustrates CA signals collected in connection with a wide-band(VEGM) sensing channel and a narrow-band (VSENSE) sensing channel andanalyzed in connection with the process of FIG. 9B in accordance withembodiments herein.

FIG. 10D illustrates an example of a CA signal that is analyzed inconnection with the process of FIG. 9B in accordance with embodimentsherein.

FIG. 10E illustrates an example of CA signals analyzed by a process forsetting upper and lower bounds on sensitivity levels in connection withthe process of FIG. 9D in accordance with embodiments herein.

FIG. 10F illustrates a portion of a CA signal processed in accordancewith the operation at in FIG. 9E in accordance with embodiments herein.

FIG. 11 illustrates a process for identifying noise in accordance withembodiments herein.

FIG. 12A illustrates an example of a CA signal segment (e.g., a VEGMsignal) that includes a QRS complex with an R-wave marker in accordancewith embodiments herein.

FIG. 12B illustrate example CA signals that are analyzed by the processof FIG. 11 in accordance with embodiments herein.

FIG. 12C illustrate example CA signals that are analyzed by the processof FIG. 11 in accordance with embodiments herein.

FIG. 12D illustrates strips of stored EGM signals collected by an ICMutilizing a conventional on-device noise detection circuit in accordancewith embodiments herein.

DETAILED DESCRIPTION

-   -   I. TERMS AND ABBREVIATIONS    -   II. OVERVIEW—1^(ST) & 2^(ND) PASS AF DETECTION/CONFIRMATION        SYSTEM & PROCESS    -   III. ALTERNATIVE EMBODIMENT—IMPROVED R-WAVE DETECTION        ALGORITHM—BRADYCARDIA AND ASYSTOLE EPISODES USING A SECOND PASS        DETECTION WORKFLOW    -   IV. ALTERNATIVE EMBODIMENT—R-WAVE DETECTION USING SELF-ADJUSTING        PARAMETERS AND PHYSIOLOGIC DISCRIMINATORS (1^(ST) & 2^(ND) PASS)    -   V. ALTERNATIVE EMBODIMENT—FULLY ADAPTIVE R-WAVE        DETECTION/CORRECTION ALGORITHM (1^(ST) & 2^(ND) PASS)    -   VI. ALTERNATIVE EMBODIMENT—NOISE DETECTION ALGORITHM FOR CA        SIGNALS SENSED BY IMPLANTABLE CARDIAC MONITOR (1^(ST) & 2^(ND)        PASS)

I. TERMS AND ABBREVIATIONS

The terms “cardiac activity signal”, “cardiac activity signals”, “CAsignal” and “CA signals” (collectively “CA signals”) are usedinterchangeably throughout to refer to an analog or digital electricalsignal recorded by two or more electrodes positioned subcutaneous orcutaneous, where the electrical signals are indicative of cardiacelectrical activity. The cardiac activity may be normal/healthy orabnormal/arrhythmic. Non-limiting examples of CA signals include ECGsignals collected by cutaneous electrodes, and EGM signals collected bysubcutaneous electrodes.

The terms “cardiac activity data set” and “CA data set” (collectively“CA data set”) are used interchangeably to refer to a data set thatincludes measured CA signals for a series of cardiac events incombination with device documented markers.

The term “marker” refers to data and/or information identified from CAsignals that may be presented as graphical and/or numeric indiciaindicative of one or more features within the CA signals and/orindicative of one or more episodes exhibited by the cardiac events.Markers may be superimposed upon CA signals or presented proximate to,and temporally aligned with, CA signals. Non-limiting examples ofmarkers may include R-wave markers, noise markers, activity markers,interval markers, refractory markers, P-wave markers, T-wave markers,PVC markers, sinus rhythm markers, AF markers and other arrhythmiamarkers. As a further non-limiting example, basic event markers mayinclude “AF entry” to indicate a beginning of an AF event, “in AF” toindicate that AF is ongoing, “AF exit” to indicate that AF hasterminated, “T” to indicate a tachycardia beat, “B” to indicate abradycardia beat, “A” to indicate an asystole beat, “VS” to indicate aregular sinus beat, “Tachy” to indicate a tachycardia episode, “Brady”to indicate a Bradycardia episode, “Asystole” to indicate an asystoleepisode, “Patient activated” to indicate a patient activated episode. Anactivity marker may indicate activity detected by activity sensor duringthe CA signal. Noise markers may indicate entry/start, ongoing, recoveryand exit/stop of noise. Markers may be presented as symbols, dashedlines, numeric values, thickened portions of a waveform, and the like.Markers may represent events, intervals, refractory periods, ICMactivity, and other algorithm related activity. For example, intervalmarkers, such as the R-R interval, may include a numeric valueindicating the duration of the interval. The AF markers indicate atrialfibrillation rhythmic.

The term “device documented marker” refers to markers that are declaredby an implantable cardiac monitor and/or implantable medical device. Anyor all of the foregoing examples of markers represent device documentmarkers. Markers may be declared based on numerous criteria, such assignal processing, feature detection and AF detection software andhardware within and/or operating on the implantable cardiac monitorand/or implantable medical device.

The term “COI” refers to a characteristic of interest within CA signals.Non-limiting examples of features of interest include an R-wave, P-wave,T-wave and isoelectric segments. A feature of interest may correspond toa peak of an individual R-wave, an average or median P, R or T-wave peakand the like.

The terms “beat” and “cardiac event” are used interchangeably and referto both normal or abnormal events.

The terms “normal” and “sinus” are used to refer to events, features,and characteristics of, or appropriate to, a heart's healthy or normalfunctioning.

The terms “abnormal,” or “arrhythmic” are used to refer to events,features, and characteristics of, or appropriate to, a un-healthy orabnormal functioning of the heart.

The term “real-time” refers to a time frame contemporaneous with anormal or abnormal episode occurrences. For example, a real-time processor operation would occur during or immediately after (e.g., withinminutes or seconds after) a cardiac event, a series of cardiac events,an arrhythmia episode, and the like.

The term “adaptive”, as used in connection with a sensitivity profile,sensitivity limit, sensitivity level or other sensing parameters, refersto an ability of the processes herein to modify the value of sensitivityand/or sensing parameters based on features within the CA signals. Thesensitivity profile parameters may include refractory period, startsensitivity, decay delay, sensitivity limit, slope of sensitivity decay,etc.

The term “sensitivity level”, as used herein, refers to a threshold thatan input CA signal must exceed for an implantable device to identify aCA signal feature of interest (e.g., an R-wave). As one non-limitingexample, software may be implemented using a programmed sensitivitylevel to declare an R-wave to be detected when the input CA signalexceeds the current programmed sensitivity level In response, thesoftware declares a device documented feature (e.g., R-wave) marker. Thesensitivity level may be defined in various manners based on the natureof the CA signals. For example, when the CA signals measure electricalactivity in terms of millivolts, the sensitivity level represents amillivolt threshold. For example, when a cardiac beat with a 0.14 mVamplitude is sensed by a device hardware, and R-wave may be detectedwhen the current sensitivity level is programmed to 0.1 mV. However,when the sensitivity level is programmed to 0.15 mV or above, a cardiacbeat with an amplitude of 0.14 mV will not be detected as an R-wave.Embodiments herein determine an adaptive sensitivity limit andsensitivity profile for the sensitivity level.

The term “turn”, as used herein to refer to characteristics of a shapeor morphology of a CA signal, shall mean changes in a direction of theCA signal. For example, the CA signal may turn by changing directionfrom a signal having a positive slope to a negative slope, or from asignal having a negative slope to a positive slope. Turns may havevarious associated characteristics such as amplitude, frequency (e.g.,number of turns per unit time) and duration (e.g., an amount of time forthe signal to exceed and drop below a desired percentage of the signalpeak).

The terms “significant” and “non-significant”, when used in connectionwith describing PVC burden, refer to an amount of PVC burden that is, oris not, sufficient to cause an AF detection algorithm to declare a falsearrhythmia episode. A small number of PVC events, and/or a collection ofPVC events that are spaced substantially apart from one another overtime, may not be sufficient to be considered “significant” as the PVCevents do not cause the AF detection algorithm to declare a falsearrhythmia episode. Alternatively, when a sufficient number of PVCevents occur within a relatively short period of time, the potentialexists that the AF detection algorithm incorrectly identifies R-waveswithin the PVC events, leading to a declaration of a false arrhythmiaepisode. For example, a 30-45 second strip of EGM signals may includeone or more PVC events that cause the AF detection algorithm of an IMDto designate a false R-wave marker. Based on the number of false R-wavemarkers in the EGM strip, the AF detection algorithm may determine thatno arrhythmia episode is present or a false arrhythmia episode ispresent.

II. OVERVIEW—1^(ST) & 2^(ND) PASS AF DETECTION/CONFIRMATION SYSTEM &PROCESS

FIG. 1 illustrates an implantable cardiac monitoring device (ICM) 100intended for subcutaneous implantation at a site near the heart. The ICM100 includes a pair of spaced-apart sense electrodes 114, 126 positionedwith respect to a housing 102. The sense electrodes 114, 126 provide fordetection of far field electrogram signals. Numerous configurations ofelectrode arrangements are possible. For example, the electrode 114 maybe located on a distal end of the ICM 100, while the electrode 126 islocated on a proximal side of the ICM 100. Additionally, oralternatively, electrodes 126 may be located on opposite sides of theICM 100, opposite ends or elsewhere. The distal electrode 114 may beformed as part of the housing 102, for example, by coating all but aportion of the housing with a nonconductive material such that theuncoated portion forms the electrode 114. In this case, the electrode126 may be electrically isolated from the housing 102 electrode byplacing it on a component separate from the housing 102, such as theheader 120. Optionally, the header 120 may be formed as an integralportion of the housing 102. The header 120 includes an antenna 128 andthe electrode 126. The antenna 128 is configured to wirelesslycommunicate with an external device 154 in accordance with one or morepredetermined wireless protocols (e.g., Bluetooth, Bluetooth low energy,Wi-Fi, etc.). The housing 102 includes various other components such as:sense electronics for receiving signals from the electrodes, amicroprocessor for processing the signals in accordance with algorithms,such as the AF detection algorithm described herein, a loop memory fortemporary storage of CA data, a device memory for long-term storage ofCA data upon certain triggering events, such as AF detection, sensorsfor detecting patient activity and a battery for powering components.

In at least some embodiments, the ICM 100 is configured to be placedsubcutaneously utilizing a minimally invasive approach. Subcutaneouselectrodes are provided on the housing 102 to simplify the implantprocedure and eliminate a need for a transvenous lead system. Thesensing electrodes may be located on opposite sides of the device anddesigned to provide robust episode detection through consistent contactat a sensor—tissue interface. The ICM 100 may be configured to beactivated by the patient or automatically activated, in connection withrecording subcutaneous ECG signals.

The ICM 100 senses far field, subcutaneous CA signals, processes the CAsignals to detect arrhythmias and if an arrhythmia is detected,automatically records the CA signals in memory for subsequenttransmission to an external device 154. The CA signal processing and AFdetection is provided for, at least in part, by algorithms embodied inor implemented by the microprocessor. The ICM 100 includes one or moreprocessors and memory that stores program instructions directing theprocessors to implement AF detection utilizing an on-board R-R intervalirregularity (ORI) process that analyzes cardiac activity signalscollected over one or more sensing channels.

FIG. 2A shows a block diagram of the ICM 100 formed in accordance withembodiments herein. The ICM 100 may be implemented to monitorventricular activity alone, or both ventricular and atrial activitythrough sensing circuitry. The ICM 100 has a housing 102 to hold theelectronic/computing components. The housing 102 (which is oftenreferred to as the “can”, “case”, “encasing”, or “case electrode”) maybe programmably selected to act as an electrode for certain sensingmodes. Housing 102 further includes a connector (not shown) with atleast one terminal 113 and optionally additional terminals 115. Theterminals 113, 115 may be coupled to sensing electrodes that areprovided upon or immediately adjacent the housing 102. Optionally, morethan two terminals 113, 115 may be provided in order to support morethan two sensing electrodes, such as for a bipolar sensing scheme thatuses the housing 102 as a reference electrode. Additionally, oralternatively, the terminals 113, 115 may be connected to one or moreleads having one or more electrodes provided thereon, where theelectrodes are located in various locations about the heart. The typeand location of each electrode may vary.

The ICM 100 includes a programmable microcontroller 121 that controlsvarious operations of the ICM 100, including cardiac monitoring.Microcontroller 121 includes a microprocessor (or equivalent controlcircuitry), RAM and/or ROM memory, logic and timing circuitry, statemachine circuitry, and I/O circuitry. The microcontroller 121 alsoperforms the operations described herein in connection with collectingcardiac activity data and analyzing the cardiac activity data toidentify AF episodes.

A switch 127 is optionally provided to allow selection of differentelectrode configurations under the control of the microcontroller 121.The electrode configuration switch 127 may include multiple switches forconnecting the desired electrodes to the appropriate I/O circuits,thereby facilitating electrode programmability. The switch 127 iscontrolled by a control signal from the microcontroller 121. Optionally,the switch 127 may be omitted and the I/O circuits directly connected tothe housing electrode 114 and a second electrode 126. Microcontroller121 includes an arrhythmia detector 134 that is configured to analyzecardiac activity signals to identify potential AF episodes as well asother arrhythmias (e.g., Tachycardias, Bradycardias, Asystole, etc.). Byway of example, the arrhythmia detector 134 may implement an AFdetection algorithm as described in U.S. Pat. No. 8,135,456, thecomplete subject matter of which is incorporated herein by reference.Although not shown, the microcontroller 121 may further include otherdedicated circuitry and/or firmware/software components that assist inmonitoring various conditions of the patient's heart and managing pacingtherapies.

The ICM 100 is further equipped with a communication modem(modulator/demodulator) 140 to enable wireless communication. In oneimplementation, the communication modem 140 uses high frequencymodulation, for example using RF, Bluetooth or Bluetooth Low Energytelemetry protocols. The signals are transmitted in a high frequencyrange and will travel through the body tissue in fluids withoutstimulating the heart or being felt by the patient. The communicationmodem 140 may be implemented in hardware as part of the microcontroller121, or as software/firmware instructions programmed into and executedby the microcontroller 121. Alternatively, the modem 140 may resideseparately from the microcontroller as a standalone component. The modem140 facilitates data retrieval from a remote monitoring network. Themodem 140 enables timely and accurate data transfer directly from thepatient to an electronic device utilized by a physician.

The ICM 100 includes sensing circuitry 144 selectively coupled to one ormore electrodes that perform sensing operations, through the switch 127to detect cardiac activity data indicative of cardiac activity. Thesensing circuitry 144 may include dedicated sense amplifiers,multiplexed amplifiers, or shared amplifiers. It may further employ oneor more low power, precision amplifiers with programmable gain and/orautomatic gain control, bandpass filtering, and threshold detectioncircuit to selectively sense the features of interest. In oneembodiment, switch 127 may be used to determine the sensing polarity ofthe cardiac signal by selectively closing the appropriate switches.

The output of the sensing circuitry 144 is connected to themicrocontroller 121 which, in turn, determines when to store the cardiacactivity data of CA signals (digitized by the A/D data acquisitionsystem 150) in the memory 160. For example, the microcontroller 121 mayonly store the cardiac activity data (from the ND data acquisitionsystem 150) in the memory 160 when a potential AF episode is detected.The sensing circuitry 144 receives a control signal 146 from themicrocontroller 121 for purposes of controlling the gain, threshold,polarization charge removal circuitry (not shown), and the timing of anyblocking circuitry (not shown) coupled to the inputs of the sensingcircuitry.

In the example of FIG. 2A, a single sensing circuit 144 is illustrated.Optionally, the ICM 100 may include multiple sensing circuits, similarto sensing circuit 144, where each sensing circuit is coupled to two ormore electrodes and controlled by the microcontroller 121 to senseelectrical activity detected at the corresponding two or moreelectrodes. The sensing circuit 144 may operate in a unipolar sensingconfiguration or in a bipolar sensing configuration. Optionally, thesensing circuit 144 may be removed entirely and the microcontroller 121perform the operations described herein based upon the CA signals fromthe ND data acquisition system 150 directly coupled to the electrodes.

The arrhythmia detector 134 of the microcontroller 121 includes anon-board R-R interval irregularity (ORI) process 136 that detects AFepisodes using an automatic detection algorithm that monitors forirregular ventricular rhythms that are commonly known to occur duringAF. The ORI process 136 may be implemented as firmware, software and/orcircuits. The ORI process 136 uses a hidden Markov Chains and Euclidiandistance calculations of similarity to assess the transitionary behaviorof one R-wave (RR) interval to another and compare the patient's RRinterval transitions to the known RR interval transitions during AF andnon-AF episodes obtained from the same patient and/or many patients. TheORI process 136 detects AF episodes over a short number of RR intervals.For example, the ORI process 136 may implement the AF detection methodsdescribed in U.S. Pat. No. 8,135,456, the complete subject matter ofwhich is incorporated herein by reference in its entirety. As explainedherein, the ORI process 136 manages a sensitivity profile of the sensorcircuit 144 during R-wave detection utilizing an automatic sensingcontrol (ASC) adjustment to determine whether the CA signal hassufficient amplitude to be analyzed for cardiac events. The ORI process136 identifies R-waves within the CA signals at points where the CAsignal crosses the sensitivity profile (outside of a refractory period).The ORI process 136 tracks RR intervals within the CA signal andidentifies AF events within the CA signal based on irregularities in theRR interval. When a sufficient number (e.g., X cardiac events out of Ycardiac events) of the cardiac events within the CA signal areidentified as AF events, the ORI process 136 declares an AF episode.

Optionally, the microcontroller 121 may also include a confirmatoryfeature detection process 137 configured to implement one or more of theoperations discussed herein, such as all or a portion of the enhancedconfirmatory AF detection process of FIG. 3 and/or all or a portion ofthe AF detection classifying and recommendation process of FIG. 4 . As afurther example, the confirmatory feature detection process 137 mayimplement one or more of the R-wave detection processes, noise detectionprocesses, P-wave detection processes and PVC detection processesdescribed in the Co-Pending Related Applications.

FIG. 2B illustrates an automatic sensing control adjustment utilized bythe ORI process 136 of the ICM 100 in accordance with embodimentsherein. FIG. 2B illustrates an example cardiac activity signal 151 afterpassing through a rectifier to convert all positive and negativedeflections within the cardiac activity signal 151 to be positivedeflections. The ORI process 136 manages the sensor circuit 144 to havea sensitivity profile 153 (denoted by a dashed line) that varies overtime.

In a basic implementation, the ORI process 136 utilizes a conventionalautomatic sensing control adjustment based on a conventional sensitivityprofile 153. The sensitivity profile 153 is defined by sensitivityprofile parameter settings corresponding to the threshold startsensitivity 161, decay delay parameter 169, maximum sensitivity 157 andslope of the sensitivity decay 165. Optionally, the sensitivity decay165 may be defined in accordance with a non-linear monotonicallychanging shape from the threshold start sensitivity 161 to the maximumsensitivity 157. The start sensitivity parameter defines a startsensitivity of the sensitivity profile. For example, the startsensitivity parameter may set a start sensitivity to a percentage of thepreceding R-wave peak amplitude. The refractory period/interval durationparameter defines a blanking interval beginning at a sensed R-wave,during which the processors do not search for a T-wave. The decay delayparameter defines the interval at which the sensitivity profilemaintains the sensitivity level at a constant level following expirationof the refractory period before the sensitivity profile beginsdecreasing. When the sensitivity profile includes a linear sensitivitylevel decline, the decay delay rate defines a slope of the linearsensitivity level decline. The maximum sensitivity limit defines alowest sensitivity level (e.g., maximum resolution) that linearsensitivity decline is allowed to reach. The sensitivity parameters arepreprogrammed to fixed values and, over the operation of the implantablemedical device (IMD), are only modified (if at all) by a clinician.

In accordance with the sensitivity profile 153, when the CA signal 151crosses the sensitivity profile 153 at starting point 155, the ORIprocess 136 treats the point 155 as a sensed R-wave and begins arefractory interval 159. No new R-wave (or T-wave) will be sensed duringthe refractory interval 159. At the end of the refractory interval 159,the sensitivity is adjusted to a threshold start sensitivity 161. Thethreshold start sensitivity 161 is defined as a percentage of the peakamplitude 163 of the QRS complex of the CA signal 151 detected duringthe refractory interval 159. The sensing circuit 144 maintains thethreshold start sensitivity 161 for a decay delay parameter 169, afterwhich the ORI process 136 begins to monotonically decrease thesensitivity (increase the resolution) of the sensing circuit 144 asdenoted by the sensitivity decay 165 within the sensitivity profile 153.The sensing circuit 144 continues to decrease the sensitivity untileither the sensitivity decay 165 reaches the maximum sensitivity 157 oran amplitude of the rectified cardiac activity signal 151 exceeds thesensor sensitivity profile 153, such as at a point 167 where a newsensed R wave is detected.

The sensitivity of the sensing circuit 144 (FIG. 2A) is continuouslyadjusted by the microcontroller 121 in accordance with the sensitivityprofile 153 over the course of an individual cardiac event. However, theconventional ORI process does not modify the parameter settings of thesensitivity profile beat by beat or on demand. sensitivity profileparameter

In accordance with embodiments herein, the values of the sensitivityparameters may be adjusted based on whether the ORI process 136 isdeemed to declare false AF detection R-waves. False AF detection mayoccur in connection with inappropriate R-wave sensing which may arisefrom under-sensing of R-waves and/or over-sensing of non-R-waves (e.g.,noise, or P-waves, or T-waves as R-waves). For example, the confirmatoryfeature detection process 137 may determine when the ORI process 136declares an undesirable number of false AF detections and in responsethereto adjust one or more sensitivity profile parameters. Additionally,or alternatively, the confirmatory feature detection process may beimplemented external to the ICM 100, such as at a local external deviceor remote server. The local external device and/or remote server maythen return, to the ICM 100, adjustments to the sensitivity profileparameters when an externally implemented confirmatory feature detectionprocess identifies an undesirable number of false AF detections.

Returning to FIG. 2A, the ICM 100 further includes an analog-to-digitalA/D data acquisition system (DAS) 150 coupled to one or more electrodesvia the switch 127 to sample cardiac activity signals across any pair ofdesired electrodes. The data acquisition system 150 is configured toacquire cardiac electrogram (EGM) signals as CA signals, convert the rawanalog data into digital data, and store the digital data as CA data forlater processing and/or telemetric transmission to an external device154 (e.g., a programmer, local transceiver, or a diagnostic systemanalyzer). The data acquisition system 150 is controlled by a controlsignal 156 from the microcontroller 121. The EGM signals may be utilizedas the cardiac activity data that is analyzed for potential AF episodes.The ACS adjustment and ORI process 136 may be applied to signals fromthe sensor circuit 144 and/or the DAS 150.

By way of example, the external device 154 may represent a bedsidemonitor installed in a patient's home and utilized to communicate withthe ICM 100 while the patient is at home, in bed or asleep. The externaldevice 154 may be a programmer used in the clinic to interrogate the ICM100, retrieve data and program detection criteria and other features.The external device 154 may be a handheld device (e.g., smartphone,tablet device, laptop computer, smartwatch and the like) that can becoupled over a network (e.g., the Internet) to a remote monitoringservice, medical network and the like. The external device 154facilitates access by physicians to patient data as well as permittingthe physician to review real-time CA signals while collected by the ICM100.

The microcontroller 121 is coupled to a memory 160 by a suitabledata/address bus 162. The programmable operating parameters used by themicrocontroller 121 are stored in memory 160 and used to customize theoperation of the ICM 100 to suit the needs of a particular patient. Suchoperating parameters define, for example, detection rate thresholds,sensitivity, automatic features, AF detection criteria, activity sensingor other physiological sensors, and electrode polarity, etc.

In addition, the memory 160 stores the cardiac activity data, as well asthe markers and other data content associated with detection ofarrhythmia episodes. The operating parameters of the ICM 100 may benon-invasively programmed into the memory 160 through a telemetrycircuit 164 in telemetric communication via communication link 166 withthe external device 154. The telemetry circuit 164 allows intracardiacelectrograms and status information relating to the operation of the ICM100 (as contained in the microcontroller 121 or memory 160) to be sentto the external device 154 through the established communication link166. In accordance with embodiments herein, the telemetry circuit 164conveys the cardiac activity data, markers and other information relatedto AF episodes.

The ICM 100 may further include magnet detection circuitry (not shown),coupled to the microcontroller 121, to detect when a magnet is placedover the unit. A magnet may be used by a clinician to perform varioustest functions of the housing 102 and/or to signal the microcontroller121 that the external device 154 is in place to receive or transmit datato the microcontroller 121 through the telemetry circuits 164.

The ICM 100 can further include one or more physiologic sensors 170.Such sensors are commonly referred to (in the pacemaker arts) as“rate-responsive” or “exercise” sensors. The physiological sensor 170may further be used to detect changes in the physiological condition ofthe heart, or diurnal changes in activity (e.g., detecting sleep andwake states). Signals generated by the physiological sensors 170 arepassed to the microcontroller 121 for analysis and optional storage inthe memory 160 in connection with the cardiac activity data, markers,episode information and the like. While shown as being included withinthe housing 102, the physiologic sensor(s) 170 may be external to thehousing 102, yet still be implanted within or carried by the patient.Examples of physiologic sensors might include sensors that, for example,activity, temperature, sense respiration rate, pH of blood, ventriculargradient, activity, position/posture, minute ventilation (MV), and soforth.

A battery 172 provides operating power to all of the components in theICM 100. The battery 172 is capable of operating at low current drainsfor long periods of time. The battery 172 also desirably has apredictable discharge characteristic so that elective replacement timecan be detected. As one example, the housing 102 employs lithium/silvervanadium oxide batteries. The battery 172 may afford various periods oflongevity (e.g., three years or more of device monitoring). In alternateembodiments, the battery 172 could be rechargeable. See for example,U.S. Pat. No. 7,294,108, Cardiac event micro-recorder and method forimplanting same, which is hereby incorporated by reference.

The ICM 100 provides a simple to configure data storage option to enablephysicians to prioritize data based on individual patient conditions, tocapture significant events and reduce risk that unexpected events aremissed. The ICM 100 may be programmable for pre- and post-trigger eventstorage. For example, the ICM 100 may be automatically activated tostore 10-120 seconds of CA data prior to an event of interest and/or tostore 10-120 seconds of post CA data. Optionally, the ICM 100 may affordpatient triggered activation in which pre-event CA data is stored, aswell as post event CA data (e.g., pre-event storage of 1-15 minutes andpost-event storage of 1-15 minutes). Optionally, the ICM 100 may affordmanual (patient triggered) or automatic activation for CA data.Optionally, the ICM 100 may afford additional programming options (e.g.,asystole duration, bradycardia rate, tachycardia rate, tachycardia cyclecount). The amount of CA data storage may vary based upon the size ofthe memory 160.

The ICM 100 may provide comprehensive safe diagnostic data reportsincluding a summary of heart rate, in order to assist physicians indiagnosis and treatment of patient conditions. By way of example,reports may include episodal diagnostics for auto trigger events,episode duration, episode count, episode date/time stamp and heart ratehistograms. The ICM 100 may be configured to be relatively small (e.g.,between 2-10 cc in volume) which may, among other things, reduce risk ofinfection during implant procedure, afford the use of a small incision,afford the use of a smaller subcutaneous pocket and the like. The smallfootprint may also reduce implant time and introduce less change in bodyimage for patients.

FIG. 2C illustrates cardiac activity data generated and stored by theICM 100 in memory 160 in accordance with embodiments herein. The CA dataset 141 is stored by the ICM in response to detection of episodes ofinterest, patient initiated instructions, physician initiatedinstructions and the like. The CA data set 141 may include, among otherthings, patient and ICM identification information 142. By way ofexample, the patient identification information may include a patientunique medical record number or other identifier, patient name and/orpatient demographic information. The ICM ID may include a serial numberor other unique identifier of the ICM, software and firmware versionnumbers, and/or a unique wireless ID. The CA data set 141 includes oneor more signal channels 143 that store CA signals collected by acorresponding sensing channel (e.g., sensor circuit 144 or DAS 150). TheCA signal channel 143 may include EGM signals for a series of cardiacbeats/events sensed by the ICM. The CA data set 141 also includes amarker channel 145 having, among other things, device documented markersidentified by the ICM 100 in connection with the CA signal. The devicedocumented markers within the marker channel 145 may include devicedocumented markers indicative of normal sinus features, AF detectedevents, AF detected episodes and the like. For example, the ORI process136 (FIG. 2A) utilizes the sensitivity profile 153 (FIG. 2B) to identifyR-waves in the CA signal.

The content of the CA signal channel 143 and marker channel 145 may bedisplayed on a display of an external device (e.g., smart phone, tabletdevice, computer, smart watch, etc.) as corresponding types of CA andmarker waveforms (e.g., in a rhythm display screen). In the presentexample, a single CA signal channel 143 is described in connection witha single CA signal. Optionally, embodiments herein may be implemented inconnection with multiple CA signal channels. For example, the ICM 100may be configured to include multiple sensing channels with differentsensing characteristics. As one example, a first sensing channel may beconfigured to perform full range signal sensing, such as in connectionwith detecting R-waves (corresponding to the CA signal channel 143). Asecond sensing channel may be configured to perform narrow range signalsensing, such as in connection with detecting P-waves which have muchsmaller amplitude in comparison to the R-waves. Optionally, multiple ECGsignals may be displayed in parallel and temporally aligned with EGM andmarker waveforms.

The CA data set 141 also includes episode statistics 147 and arrhythmiadiagnostics 149. The episode statistics 147 may be presented in a windowon a user interface to list various statistical data for any or allepisodes recorded by the ICM 100 since the episode and CA data storagewere last cleared. Optionally, the episode statistics 147 may also listthe number of inhibited VT diagnoses due to arrhythmia qualifiers, suchas a bigeminal rhythm qualifier, and/or other rhythm discriminators. Asfurther non-limiting examples, the episode statistics 147 may alsoinclude a date of a last programmer session, date of the last ICMinterrogation, the date of the presently stored episodes and the datewhen EGMs were last cleared from the ICM and the like.

Optionally, the a CA data set 141 may also include a confirmation log147A that may be calculated in real-time or off-line in accordance withembodiments herein. For example, the original CA data set 141 may begenerated by the ICM based on the ORI process described herein. Once theCA data set 141 is telemetered from the ICM to a local external deviceand/or remote server, the CA data set 141 is analyzed utilizing a secondpass confirmation arrhythmia detection process (e.g., FIGS. 3 and 4 ).The second pass confirmation detection process generates a confirmationlog that includes, among other things, confirmatory markers,confirmatory episode statistics and confirmatory arrhythmia diagnosticsthat may differ from or be similar to the original episode statistics147 and arrhythmia diagnostics 149. In certain instances, it may bedesirable to return the confirmation log information to the ICM (e.g.,FIG. 7 ). The information from the confirmation log may be telemeteredback to the ICM from the local external device and/or remote server. TheICM may then store the confirmation log 147A in connection with acorresponding CA data set.

In the event that an ICM is provided with certain security features thatprevent an external device (e.g., cell phone or local monitoring device)from directly changing sensitivity profile parameter settings and/orwriting to any or at least certain sections of the memory within theICM. For example, the security features may prevent an external devicefrom writing over-sensitivity profile parameter settings and/or over theAF statistics and diagnostics that are generated and stored on the ICM.Optionally, as a workaround, the confirmation log may be written to amore flexible section of memory within the ICM (also referred to as anexternal device accessible section), along with header and/or metadatainformation tying the confirmation log to a particular portion of the CAdata.

FIGS. 2D and 2E illustrate screenshots of displays in which episodestatistics and arrhythmia diagnostics may be presented to a physician inaccordance with an embodiment herein. The arrhythmia diagnostics 148 mayrepresent cumulative diagnostic information for a period of time, suchas when the diagnostics data is last cleared from the ICM. Thearrhythmia diagnostics 149 may include various information concerningheart rate, such as ventricular heart rate histograms, dates and timesof last programmer sessions, diagnostic data last read, diagnostic datalast cleared and the like. The arrhythmia diagnostics 149 may alsoinclude AF diagnostics, such as AF burden 149A, AF summaries, AFstatistical data 149B, dates and times of last programmer session, lasttime the AF diagnostic data were read, last time the AF diagnostic datawas cleared and the like. By way of example, AF burden may be displayedin an AF diagnostics window of a computing device formatted as one ormore bar graphs of a percentage of time (as shown in FIG. 2E) that thepatient experienced AF during a predetermined period of time (e.g., eachday, each week, each month). The AF burden may show a percentage of timethat the patient was in AF since the AF diagnostics data were lastcleared. The AF summary may include one or more graphs of meanventricular heart rate and a duration of AF episodes since the AFdiagnostic data were last cleared. The AF diagnostic data may accruevarious cumulative totals concerning AF episodes detected and/or storedsince the AF diagnostic data were last cleared. The AF statistics mayinclude, among other things, a total number of AF episodes, AF burdentrends, AF episode duration histograms, mean ventricular rate during AFand the like.

As explained herein, an enhanced confirmatory AF detection process isimplemented to analyze the results of the baseline analysis performed bythe ORI process in the ICM. The enhanced confirmatory AF detectionprocess determines whether AF episodes declared by the ICM are true orfalse, and updates the AF diagnostics in connection there with. Next,various processes are described in connection with embodiments hereinthat are performed by one or more of the circuits, processors and otherstructures illustrated in the figures and described in thespecification.

FIG. 3 shows a high-level workflow for an enhanced confirmatory AFdetection process implemented in accordance with embodiments herein. Byway of example, the operations of FIG. 3 may be implemented, as aconfirmatory process, where cardiac activity signals have beenpreviously analyzed by an AF detection module, such as the ORI processdescribed in connection with FIGS. 2A and 2B. The process may initiatethe operations of FIG. 3 in an attempt to verify whether one or moreepisodes in a CA data set, are in fact an AF episode or a normalrhythmic/sinus episode. Optionally, the operations of FIG. 3 may beimplemented in connection with a CA data set that has not beenpreviously analyzed for potential AF episodes. The operations of FIG. 3may be implemented as part of a local or distributed system, such as bythe microcontroller 121 of the ICM, by a local external device and/or aremote server.

At 302, one or more processors of the system obtain a cardiac activity(CA) data set including CA signals recorded in connection with a seriesof cardiac events. The CA data includes device documented arrhythmicmarkers including identifying AF entry and/or exit within the series ofcardiac events. The CA data also includes device documented rhythmicmarkers (e.g., R-wave) to identify the cardiac beats sensed by thedevice within the series of cardiac events. The CA data also includedevice documented activity and noise markers to identify periods of timeunder significant physical activity and/or noise interrupt within theseries of cardiac events. All device documented markers are declared anddesignated by the ICM utilizing the ORI process to analyze the CAsignals.

For example, the cardiac activity data may be obtained by an externalmonitoring device or ICM that includes electrodes that sense CA signals,such as electrocardiogram (ECG) signals and/or intra-electrocardiogram(EGM) signals. The ECG and/or EGM signals may be collected by asubcutaneous ICM that does not include a transvenous lead or otherwiseexperiences difficulty in sensing P-waves and/or R-waves. The cardiacactivity data may have been previously acquired and stored in memory ofan implantable or external monitoring device, implantable or externaltherapy delivery device, programmer, workstation, healthcare network orother system. When the cardiac activity data has been previouslyacquired, the obtaining operation at 302 represents accessing andreading the previously stored cardiac activity data.

The operations of FIG. 3 may be staged to be performed upon the CA dataat various times, such as in real time (e.g., during or shortly after apatient experiences an episode) or at any time after storage of the CAdata. The operations of FIG. 3 may be performed by devices and systemsat various proximity to a patient with the ICM. For example, the CA datamay be read out of an ICM and transmitted to a local portable externaldevice (e.g., smartphone, table computer, laptop computer, smartwatch,etc.), where the local portable external device locally implements allor a portion of the operations described in connection with FIG. 3 whilein close proximity to the patient. Additionally, or alternatively, theCA data may be read out of the ICM to a local portable external deviceand transmitted to a remote server, medical network, physician computerand the like, which implements all or a portion of the operationsdescribed in connection with FIG. 3 remote from the patient.Additionally, or alternatively, the CA data may be read from the ICM bya programmer device, such as during a patient visit to a physician,where the programmer device implements all or a portion of theoperations described in connection with FIG. 3 during or after apatient-doctor visit.

The CA data may include CA signals for a series of cardiac eventsspanning over various periods of time. As one example, one segment orset of the cardiac activity data may be collected for an interval thatis 30 seconds to 5 minutes in length and that includes one or more ICMdeclared AF episodes. As another example, one segment or set of thecardiac activity data may be collected for an interval that begins 10-60seconds before an episode of interest (e.g., an AF episode) and thatends 10-60 seconds after the episode of interest. A CA data set mayinclude one or multiple AF episodes. The duration of a CA data set maybe programmed for a predetermined period of time based on detection ofAF episodes and/or based on other criteria. The predetermined period oftime may be programmed by a clinician, or automatically updated by oneor more processors throughout operation. By way of example, thepredetermined period of time may correspond to one minute, 30 minutes,one hour or otherwise. The CA data obtained at 302 may correspond to onedetected AF episode and/or multiple detected AF episodes. The CA dataset obtained at 302 may correspond to one continuous series of cardiacevents (e.g., 1 continuous series for 30 seconds to 5 minutes) and/orseparate sets of cardiac events (3-10 separate series, each for 30seconds to 3 minutes of cardiac events).

Collection and analysis of CA signals by the ICM may be initiatedautomatically when the ICM detects an episode of interest. Additionally,or alternatively, the ICM may collect and analyze CA signals in responseto a user-initiated instruction. For example, a user may utilize a smartphone or other portable device to establish a communications sessionwith the ICM and instruct the ICM to begin to collect and analyzecardiac signals, such as when the patient is experiencing discomfort,feeling faint, a rapid heart rate, etc.

At 304 to 320, the one or more processors determine whether the on-boardRR interval irregularity process (implemented by the ICM declared one ormore false positive AF episodes, such as due to under-sensing orover-sensing features within the CA signal. The operations at 304 to 320generally perform an R-wave enhancement and feature rejection (EFR)process. The EFR process enlarges or exaggerates features of interest(e.g., R-wave) within the CA signal and optionally suppresses at leastcertain features not of interest (e.g., non-R-wave features such asnoise, T-waves) to obtain confirmatory feature markers. The EFR processapplies a series of tests to confirm or reject alternative conditionsthat a patient may have experienced. The operations at 306 to 320confirm or reject a presence or absence of certain rhythmic, physiologicand non-physiologic (e.g., noise) features within the CA data.Non-limiting examples of the features, for which the process searchesinclude noise, R-wave changes, P-waves, and post ventricularcontractions.

At 304, the one or more processors analyze the CA data for noise andpass or remove segments of the CA signal for select cardiac events basedon a noise level within the corresponding segment of the CA signal. Thenoise is identified based on noise discrimination parameters that areset to a desired sensitivity level. While the sensitivity of the noisedetection process at 304 may be adjusted, the sensitivity of the noisedetection process at 304 is more selective than the on-board noisedetection circuit in the ICM. For example, at 304, the one or moreprocessors may implement the noise detection process described in one ormore of the Co-Pending Related Applications referred to above, filedconcurrently on the same day as the present application. For example,the operation at 304 generally represents a software based evaluation ofthe CA data to detect noise. The software based evaluation can bedeveloped in a manner that is tailored to AF detection such that thesoftware-based noise rejection is more sensitive in connection withidentifying or removing unduly noisy CA signal segments that in turngive rise to inappropriate R-wave detection, leading to false AFepisodes declaration by the ICM. The original CA data processed inconnection with FIG. 3 results from the onboard ORI process of the ICM.The onboard ORI process processes incoming signals that have firstpassed through a hardware-based noise detect that applies noisediscrimination the hardware-based noise detector is not as sensitive as,and not as adaptable as, the software based noise discriminationimplemented at 304. Also, depending upon a complexity of thesoftware-based noise discrimination, processors of an ICM may not have asufficient processing power to implement the software noisediscrimination. The extent to which the software-based noisediscrimination may be implemented on an ICM depends in part upon thesensitivity level desired. For example, the discrimination parametersmay be set to a very “conservative” level such that the noise detectoronly eliminates CA signals for cardiac events that include a substantialamount of noise (e.g., the signal to noise ratio is less than or equalto 50%). Levels for the noise discrimination parameters may be adjustedto eliminate more cardiac events that include relatively intermediatelevels of noise (e.g., the signal to noise ratio is between 75% and90%). The noise discriminator passes CA signals for cardiac events thathave less noise than the level defined by the noise discriminationparameters.

Optionally, at 304, when the noise level is sufficiently high (e.g.,satisfying a threshold), the initial AF diagnosis/declaration by the ICMmay be overridden. For example, when the noise level exceeds a thresholdin connection with an AF episode declared by the ICM, the processors maycancel the AF episode declaration and reset any counters set inconnection there with. Optionally, as explained below in connection withFIG. 11 , embodiments herein may declare a segment of the CA signals torepresent a noisy segment, and remove the noisy segment to form noisecorrected CA signals. The operations at 306-320 then apply aconfirmatory arrhythmia detection process to the noise corrected CAsignals. Optionally, when a sufficiently large portion of a CA data setis declared to be noisy, the entire CA data set (e.g., a 30 second EGMstrip) may be rejected, and flow returns to 302, where a new CA data setis obtained.

At 306, the one or more processors apply a feature enhancement processto form modified CA signals in which sinus features of interest areenlarged or exaggerated relative to the original/baseline CA signals.Optionally, at least certain features not of interest (e.g., noise,T-waves) are reduced or suppressed relative to the baseline CA signalsin order to generate the confirmatory feature (e.g., R-wave) marker. Forexample, at 306, the one or more processors may implement the featureenhancement process described in one or more of the Co-Pending RelatedApplications referred to above, filed concurrently on the same day asthe present application.

At 307, the one or more processors analyze the modified CA signalutilizing a confirmatory feature detection process. For example, at 306,the one or more processors may implement, as the confirmatory featuredetection process, the R-wave detection processes described in one ormore of the Co-Pending Related Applications referred to above, and filedconcurrently on the same day as the present application. The processorsanalyze the modified CA signal to identify R-waves, and store a set ofconfirmatory feature markers separate and distinct from the devicedocumented (DD) feature markers.

At 308, the one or more processors determine whether the confirmatoryfeature markers match or differ from the DD feature markers. Forexample, the determination at 308 may be based on a simple count of thenumber of DD feature markers as compared to a count of the number ofconfirmatory feature markers. Additionally, or alternatively, thedetermination at 308 may determine whether the confirmatory featuredetection process identified confirmatory feature markers (e.g.,R-waves) from the CA signals that were not identified by the ORI processor displaced significantly. For example, the DD and confirmatory featuremarkers for the CA data may be aligned temporally and compared toidentify differences.

Differences may occur due to various reasons. For example, the ORIprocess may under-sense R-waves, while the confirmatory featuredetection process properly identifies a feature of interest in themodified CA signal as an R-wave. As another example, the ORI process mayover sense R-waves, while the confirmatory feature detection processproperly determines that no R-wave is present in a particular segment ofthe CA signal. Additionally, or alternatively, a difference may bedeclared when the ORI process and confirmatory feature detection processboth declare an R-wave for a specific cardiac event, but the DD andconfirmatory R-waves are temporally offset from one another in time bymore than a desired R-wave offset threshold.

When the process determines at 308 that a difference or change existsbetween the confirmatory and DD feature markers, flow moves to 310. Whenthe process determines that no difference or change exists between theconfirmatory and DD feature markers, flow moves to 312. At 310 the oneor more processors identify instability in the confirmatory featuremarkers. At 310, the one or more processors determine whether theinstability within the confirmatory feature marker indicates AF. Theprocessors determine the presence or absence of instability by analyzingvariation in the RR intervals between the confirmatory features markers,such as using the processors described in the Co-Pending RelatedApplication and/or the '456 patent. If the instability/variation equalsor is below a stability threshold, the segment of the CA signal isconsidered to exhibit a stable feature-to-feature interval that does notindicate AF. Consequently, flow moves to 316. Alternatively, when theinstability is above the instability threshold, the analysis of the CAsignal segment is considered to exhibit an unstable feature-to-featureinterval. Consequently, flow moves to 312.

At 316, when AF is not indicated, the one or more processors classify anepisode in the CA data set to be a DD false positive or false detection.At 316, the one or more processors may perform additional operations,such as setting one or more flags to track the declaration of DD falsepositives by the ORI process on the ICM. Additionally, or alternatively,at 316, the one or more processors may reverse a diagnosis of AF, adjustvarious statistics tracking the patient's behavior and the like. Forexample, the AF diagnostics (e.g., 149 in FIG. 2C) may be updated tocorrect for false AF detection. Additionally, or alternatively, a memorysegment within the ICM that includes the CA data set associated with afalse AF detection may be set to have a lower priority. Reassignment ofpriority levels to different memory segments may be utilized inconnection with overwriting memory segments during future use. Forexample, when the CA data memory of the ICM approaches or becomes full,the memory segment assigned the lowest priority may then be overwrittenfirst when the ICM detects new AF episodes.

When flow advances to 312, the potential still exists that the CAsignals does not include an AF episode. Therefore, the process of FIG. 3performs additional analysis upon the CA data. At 312, the one or moreprocessors perform a P-wave detection operation to determine whetherP-waves are present within the CA signal segment being analyzed. Forexample, at 312, the one or more processors may implement the P-wavedetection process described in one or more of the Co-Pending RelatedApplications referred to above, and filed concurrently on the same dayas the present application. When a P-wave is identified to be present inthe CA signal, the process determines that the presence of a P-waveindicates that the current episode is not an AF episode even though RRinterval irregularity may be present. Accordingly, flow moves to 316.

Alternatively, at 312 when the one or more processors determine that noP-waves are present within the CA signal, a potential still remains thatthe CA signal does not correspond to an AF episode. Accordingly, flowadvances to 318 where additional analysis is applied to the CA data set.At 318, the one or more processors apply a morphology based prematureventricular contraction (PVC) detection operation. For example, at 318,the one or more processors may implement the QRS complex morphologybased PVC detection process described in one or more of the Co-PendingRelated Applications referred to above, and filed concurrently on thesame day as the present application. The processors determine whether aQRS complex morphology has varied beyond a morphology variationthreshold. Variation in the R-wave morphology beyond the morphologyvariation threshold provides a good indicator that the cardiac eventsinclude one or more PVC. When the cardiac events include a sufficientnumber of PVCs, the process may attribute an R-R interval variation to(and indicative of) PVCs or non-atrial originated beats that lead tosignificantly different R-R intervals, and not due to (or indicative of)an AF episode. Accordingly, when the R-wave morphology exceeds themorphology variation threshold, flow returns to 316, where the processperforms the operations described herein. At 316, one or more flags maybe set to indicate that the false AF detection was declared due to oneor more PVCs present within the CA data. Additionally, or alternatively,a diagnosis may be changed from AF episode to PVC episode. The number ofPVC may vary that are needed to achieve an R-wave morphology variationat 318 sufficient for flow to branch to 316 (e.g., declare a false AFdetection).

At 318, alternatively, when the R-wave morphology does not exceed themorphology variation threshold, the process interprets the condition asan indicator that the cardiac events do not include significant numberof PVCs. Thus, flow moves to 320. At 320, the one or more processorsconfirm a device documented AF episode and records the current episodeto remain as originally declared by the ORI process.

Optionally, the sequence of operations discussed in connection with FIG.3 may be changed and/or some of the operations may be omitted dependingon computational and performance objectives. For example, it may bedetermined that a low probability exists that a particular patient (orICM) experiences PVCs that cause false AF detection, and thus, theprocess of FIG. 3 may omit the PVC detection operation at 318.Additionally, or alternatively, it may be determined that a lowprobability exists that an ICM is incorrectly detecting P-waves asR-waves that would cause false AF detection, and thus, the process ofFIG. 3 may omit the P-wave detection operation at 312.

Additionally, or alternatively, it may be determined that lessprocessing time/power is utilized to identify P-waves (operations at312) and/or PVCs (operations at 318) that cause false AF detection, ascompared to R-wave detection and analysis of RR interval stability(operations at 306-310). Accordingly, the P-wave and/or PVC detectionoperations may be performed before the R-wave detection and analysis. Inthe present example, in the event a P-wave or PVC is detected, theprocess may declare a CA data set to include a false AF detectionwithout performing the further computations for R-wave detection andanalysis.

Optionally, the operations at 308-318 may be modified to not representbinary branches between alternative paths. Instead, the decisions atoperations 308-318 may result in a score or a vote, rather than a binary“AF” or “not AF”. The vote or score may be variable based upon a degreeto which the feature of interest in the confirmatory analysis matchesthe determination from the original ORI process. Additionally, oralternatively, the vote or score may be based on a degree to which thefeature of interest from the confirmatory analysis matches one or morebaseline values. The votes or scores may be used in conjunction withother AF detection algorithms in order to find a probability that an AFepisode has occurred.

The operations of FIG. 3 may be repeated periodically or in response todetection of particular criteria, such as detection of potential atrialfibrillation episodes or otherwise.

The operations of FIG. 3 afford a powerful, sophisticated process toconfirm AF detection within ECG and EGM signals in a non-real timemanner. The AF detection confirmation processes described herein mayutilize computationally expensive analysis that may otherwise not be tobe implemented in an on-board circuit within an ICM, either due tomemory and power constraints, processing power constraints, and/or aninability to complete the analysis in real time.

Optionally, the operations of one or more of the stages within theprocess of FIG. 3 may be adapted to run in ICM firmware, althoughfirmware implementations may exhibit different overall performance. In afirmware implementation, a similar form of step-by-step discriminationon existing AF episodes may be achieved. Alternatively, some or all ofthe features may be adapted for real-time use and set as additional oralternative signals. For example, the determinations at 306-318 mayproduce factors that are applied to an AF probability and sudden onsetdetermination as AF detection criteria.

FIG. 4 illustrates a flow chart for classifying AF detection anddeveloping recommendations for sensitivity profile parameter settings inaccordance with embodiments herein. For example, the operations of FIG.4 may be performed at 316 and/or 320 in FIG. 3 and/or at other points inthe processes described herein. The operations of FIG. 4 build and/oradd to a confirmation log that tracks and records the differences andsimilarities between the results of the EFR and ORI processes. Theconfirmation log may be stored together with, or separate from, theunderlying baseline CA data set and/or the modified CA data set.Optionally, the confirmation log may not represent a separate file, butinstead merely represent parameter settings or other informationappended to the original or modified CA data set. For example, theconfirmation log may be saved as metadata or otherwise appended to theCA data set.

At 402, the one or more processors of the system determine whether theEFR process identified one or more false AF detection by the ORI processapplied by the ICM. When the EFR process and the ORI process detect acommon or similar number/degree of AF episodes in the CA data set, flowmoves to 404. At 404, the one or more processors record a match betweenthe results of the EFR and ORI processes. The match is stored in theconfirmation log. When the EFR process identifies a false AF detectionthat was declared by the ORI process, flow moves to 406.

At 406, the one or more processors classify the false AF detection intoone of multiple different categories. Non-limiting examples of thecategories include noise, inappropriate sensing, irregular sinus rhythm,frequent PVCs and the like. The processors may classify the false AFdetection as noise when the baseline CA data set is determine to have anexcessive amount of noise (at 302). For example, the excessive amount ofnoise may be determined when a number of cardiac events that areremoved/suppressed (at 304, 312, 318) exceeds a threshold and/or exceedsa percentage of the total number of cardiac events in the CA data set.The processors may classify the false AF detection as inappropriatesensing when the feature detection (at 306) determines that the CA dataincludes more or few features of interest (e.g., under-sensed R-waves orover-sensed false R-waves). The processors may classify the false AFdetection as sinus rhythm when the P-wave detection (at 312) determinesthat the CA data set includes one or more P-waves. The processors mayclassify the false AF detection as frequent PVCs when the PVC detection(at 318) determines that the CA data exceeds a PVC threshold.

At 408, the one or more processors record the classification identifiedat 406 in the confirmation log. At 410, the one or more processorsdetermine whether additional guidance is to be provided for settingsensitivity profile parameters of the ICM. For example, the processors,at 410, may determine whether an extent or degree of the false R-waveand AF detection (e.g., number of under-sensed R-waves, number ofP-waves (as well as T-wave or noise artifact) classified as R-waves,number of frequent PVCs) exceeds a threshold that justifies adjustingone or more sensitivity profile parameters of the ICM. When sensitivityprofile parameter adjustments can be made, flow moves to 412. Otherwise,flow continues to 414.

When the extent or degree of the false R-wave and AF detection warrantsa parameter adjustment, the sensitivity profile parameter adjustment isdetermined based in part on the classification at 406. At 412, the oneor more processors declare an adjustment to the sensing parameters basedon a nature and/or extent of the false R-wave and AF detection. Forexample, when a false AF detection is classified as due to inappropriatesensing, the processors may declare the sensitivity profile parameteradjustment to be an increase or decrease in the feature (e.g., R-wave)detection threshold. As another example, the processors may declare thesensitivity profile parameter adjustment to be an increase in the R-wavedetection threshold when P-waves are identified as R-waves by the ORIprocess. As another example, the processors may declare the sensitivityprofile parameter adjustment to be an increase in the decay delay valuewhen the ORI process over senses T-waves and designates the T-waves tobe R-waves. The sensitivity profile parameter adjustment is saved in theconfirmation log. Optionally, the confirmation log may also maintain aPVC count.

The increase or decrease in the sensitivity profile parameter adjustmentmay be a predefined step (e.g., increase threshold by X mV or Y %).Optionally, the increase or decrease may be based on an extent or natureof the false R-wave and AF detection. For example, when the ORI processunder-sensed multiple R-waves in the CA data set, the process maydecrease the R-wave detection threshold by a larger factor as comparedto when the ORI process under-senses one or a few R-waves out ofmultiple R-waves. As another example, a decay delay value adjustmentand/or refractory period value adjustment may be determined based inpart on a number of T waves sensed as R-waves, a timing between the Twaves and corresponding preceding R-waves, and/or a peak amplitude ofthe T waves relative to the sensing sensitivity at the time the T-waveis detected.

Optionally, the one or more processors may identify additional oralternative sensitivity profile parameter adjustments based on adatabase of sensitivity profile parameter settings that are correlatedto cardiac activity data for a patient population. For example, adatabase may be maintained of EGM or ECG data segments collected inconnection with numerous patients that experienced AF, sinus rhythmsand/or other arrhythmias, where the EGM/ECG data segments are correlatedwith sensitivity profile parameter settings that are used by amonitoring device to collect the EGM or ECG data. The patient populationdatabase may also indicate which sensitivity profile parameter settingsachieved desired results and which sensitivity profile parametersettings did not achieve desired results. The database may furtherinclude quality indicators indicative of whether the sensitivity profileparameter settings were deemed to collect good or accurate results(e.g., correctly sense R-waves without over-sensing P-waves or T waves,and correctly sense all R-waves without under-sensing of R-waves withsmaller amplitude). The database may further include quality indicatorsindicative of whether the sensitivity profile parameter settings weredeemed to accurately declare AF detection in a high percentage of theinstances of AF. The quality indicators may be automatically enteredbased on automated analysis of the data within the database and/orentered by physicians or other medical personnel as sensitivity profileparameter settings are adjusted for individual patients. The databasemay be available on a medical network, through a cloud computing serviceand/or other local or remote source.

At 414, the one or more processors compare the current false AFdetection, modified CA data set and/or baseline CA data to a database ofthird-party CA data sets and false/valid AF detections for otherpatients. The processors may identify matches or similarities betweenthe false/valid AF detection, modified CA data set and/or baseline CAdata set, for the current patient, and the corresponding type of AFdetections and third-party CA data set from the database of the largerpopulation. When no match occurs, the operations of FIG. 4 end.Alternatively, when one or more matches occur between the current CAdata set and the patient population database, flow moves to 416. At 416,the one or more processors identify additional or alternativesensitivity profile parameter adjustments to record in the confirmationlog for the present patient based on the matches or similar cases fromthe database and the present patient.

The sensitivity profile parameter adjustments, in the confirmation log,may be presented on a display of a mobile device, computer, workstation,etc., as a suggestion or option ICM for the physician or other medicalpersonnel to apply to a current. Optionally, the sensitivity profileparameter adjustments may be pushed and uploaded to the ICM from a localportable external device and/or a remote medical network. Thesensitivity profile parameter adjustments may be pushed to the ICM atthe direction of the physician or other medical personnel, after thephysician or medical personnel has reviewed the baseline and/or modifiedCA data (with R-wave and AF markers) and other statistical informationconcerning one or more episodes experienced by the patient. Additionalor alternatively, the sensitivity profile parameter adjustments may beautomatically pushed and uploaded to the ICM at the conclusion of theoperations of FIG. 4 , such as when the adjustment is within apredetermined limit.

FIG. 5 illustrates a system level diagram indicating potential devicesand networks that utilize the methods and systems herein. For example,an implantable cardiac monitoring device (ICM) 502 may be utilized tocollect a cardiac activity data set. The ICM 502 may supply the CA dataset (CA signals and DD feature markers) to various local externaldevices, such as a tablet device 504, a smart phone 506, a bedsidemonitoring device 508, a smart watch and the like. The devices 504-508include a display to present the various types of CA signals, markers,statistics, diagnostics and other information described herein. The ICM502 may convey the CA data set over various types of wirelesscommunications links to the devices 504, 506 and 508. The ICM 502 mayutilize various communications protocols and be activated in variousmanners, such as through a Bluetooth, Bluetooth low energy, WiFi orother wireless protocol. Additionally, or alternatively, when a magneticdevice 510 is held next to the patient, the magnetic field from thedevice 510 may activate the ICM 502 to transmit the cardiac activitydata set and AF data to one or more of the devices 504-508.

The processes described herein for analyzing the cardiac activity dataand/or confirm AF detection may be implemented on one or more of thedevices 504-508. Additionally, or alternatively, the ICM 502 may alsoimplement the confirmatory processes described herein. The devices504-508 may present the CA data set and AF detection statistics anddiagnostics to clinicians in various manners. As one example, AF markersmay be illustrated on EGM signal traces. AF and sinus markers may bepresented in a marker channel that is temporally aligned with originalor modified CA signals. Additionally, or alternatively, the duration andheart rate under AF may be formatted into histograms or other types ofcharts to be presented alone or in combination with CA signals.

FIG. 6 illustrates a distributed processing system 600 in accordancewith embodiments herein. The distributed processing system 600 includesa server 602 connected to a database 604, a programmer 606, a localmonitoring device 608 and a user workstation 610 electrically connectedto a network 612. Any of the processor-based components in FIG. 6 (e.g.,workstation 610, cell phone 614, local monitoring device 616, server602, programmer 606) may perform the processes discussed herein.

The network 612 may provide cloud-based services over the internet, avoice over IP (VoIP) gateway, a local plain old telephone service(POTS), a public switched telephone network (PSTN), a cellular phonebased network, and the like. Alternatively, the communication system maybe a local area network (LAN), a medical campus area network (CAN), ametropolitan area network (MAN), or a wide area network (WAM). Thecommunication system serves to provide a network that facilitates thetransfer/receipt of data and other information between local and remotedevices (relative to a patient). The server 602 is a computer systemthat provides services to the other computing devices on the network612. The server 602 controls the communication of information such ascardiac activity data sets, bradycardia episode information, asystoleepisode information, AF episode information, markers, cardiac signalwaveforms, heart rates, and device settings. The server 602 interfaceswith the network 612 to transfer information between the programmer 606,local monitoring devices 608, 616, user workstation 610, cell phone 614and database 604. The database 604 stores information such as cardiacactivity data, AF episode information, AF statistics, diagnostics,markers, cardiac signal waveforms, heart rates, device settings, and thelike, for a patient population. The information is downloaded into thedatabase 604 via the server 602 or, alternatively, the information isuploaded to the server 602 from the database 604. The programmer 606 mayreside in a patient's home, a hospital, or a physician's office. Theprogrammer 606 may wirelessly communicate with the ICM 603 and utilizeprotocols, such as Bluetooth, GSM, infrared wireless LANs, HIPERLAN, 3G,satellite, as well as circuit and packet data protocols, and the like.Alternatively, a telemetry “wand” connection may be used to connect theprogrammer 606 to the ICM 603. The programmer 606 is able to acquire ECG622 from surface electrodes on a person (e.g., ECGs), electrograms(e.g., EGM) signals from the ICM 603, and/or cardiac activity data, AFepisode information, AF statistics, diagnostics, markers, cardiac signalwaveforms, atrial heart rates, device settings from the ICM 603. Theprogrammer 606 interfaces with the network 612, either via the internet,to upload the information acquired from the surface ECG unit 620, or theICM 603 to the server 602.

The local monitoring device 608 interfaces with the communication systemto upload to the server 602 one or more of cardiac activity data set, AFepisode information, AF statistics, diagnostics, markers, cardiac signalwaveforms, heart rates, sensitivity profile parameter settings anddetection thresholds. In one embodiment, the surface ECG unit 620 andthe ICM 603 have a bi-directional connection 624 with the local RFmonitoring device 608 via a wireless connection. The local monitoringdevice 608 is able to acquire cardiac signals from the surface of aperson, cardiac activity data sets and other information from the ICM603, and/or cardiac signal waveforms, heart rates, and device settingsfrom the ICM 603. On the other hand, the local monitoring device 608 maydownload the data and information discussed herein from the database 604to the surface ECG unit 620 or the ICM 603.

The user workstation 610 may be utilized by a physician or medicalpersonnel to interface with the network 612 to download cardiac activitydata and other information discussed herein from the database 604, fromthe local monitoring devices 608, 616, from the ICM 603 or otherwise.Once downloaded, the user workstation 610 may process the CA data inaccordance with one or more of the operations described above. The userworkstation 610 may upload/push settings (e.g., sensitivity profileparameter settings), ICM instructions, other information andnotifications to the cell phone 614, local monitoring devices 608, 616,programmer 606, server 602 and/or ICM 603. For example, the userworkstation 610 may provide instructions to the ICM 603 in order toupdate sensitivity profile parameter settings when the ICM 603 declarestoo many false AF detections.

The processes described herein in connection with analyzing cardiacactivity data for confirming or rejecting AF detection may be performedby one or more of the devices illustrated in FIG. 6 , including but notlimited to the ICM 603, programmer 606, local monitoring devices 608,616, user workstation 610, cell phone 614, and server 602. The processdescribed herein may be distributed between the devices of FIG. 6 .

FIG. 7 illustrates examples of communication sessions between the ICM, alocal external device, a remote device and a server/database inaccordance with embodiments herein. For convenience, reference is madeto the devices of FIGS. 5 and 6 , in connection with FIG. 7 . Forexample, the local device may represent a cell phone 614, smart phone506, bedside monitor 508 or local monitoring device 608, 616, while theremote device may represent a workstation 610, programmer 606, or tabletdevice 504.

During an AF detection and confirmation session 701, at 702, an ICM 100provides a CA data set to a local device. At 704, the local deviceutilizes the EFR and confirmatory feature detectors processes describedherein to analyze at least a portion of the CA signals to identify falseAF detection. The false AF detections are used to generate or update aconfirmation log 706. As described herein, the confirmation log 706 mayinclude a log of the “false positive” episode counts from the originalCA data set. The confirmation log 706 may include, among other things,confirmatory markers, confirmatory episode statistics and confirmatoryarrhythmia diagnostics that may differ from or be similar to theoriginal episode statistics 147 and arrhythmia diagnostics 149 (FIG.2C). The confirmation log 706 may also include correctivecharacterizations of individual events that were mischaracterized in theoriginal CA data.

In certain instances, it may be desirable to return the confirmation log706 information to the ICM as denoted at 703. In certainimplementations, an ICM is provided with certain security features thatprevent an external device (e.g., cell phone or local monitoring device)from directly changing sensitivity profile parameter settings and/orwriting to any or at least certain sections of the memory within theICM. For example, the security features may prevent an external devicefrom writing over-sensitivity profile parameter settings and/or over theAF statistics and diagnostics that are generated and stored on the ICM.

Optionally, as a workaround, at 703, the confirmation log 706 may bewritten to a more flexible section of memory within the ICM (alsoreferred to as an external device accessible section), along with headerand/or metadata information tying the confirmation log 706 to aparticular portion of the CA data. Additionally, or alternatively, at704, the local external device may pass the confirmation log 706 to oneor more remote devices and optionally to the database and server. Theconfirmation log 706 may be written to memory of an external device thatinteracts directly and regularly with the ICM, such as cell phone 614,local monitoring device 608, 616 and the like. The confirmation log 706may be associated with particular CA data sets, such as based on time ofdata acquisition.

Optionally, a remote pairing session 708 may be performed between CAdata on an ICM and locally externally stored confirmation logs. Forexample, the local external device may be directed to initiate a datatransfer/download from the ICM, such as at 710, at a point in timeseparate from and after performing the AF detection confirmationprocesses described herein. The local external device receives the CAdata set at 712 and determines, at 714, that the CA data set has alreadybeen analyzed to confirm AF detection. At 716, the local external deviceidentifies a confirmation log 706 stored at the local external devicethat corresponds to the CA data set, and at 716, appends theconfirmation log 706 to the associated CA data set, such as based ontime of data acquisition. The cumulative information of the CA data setand confirmation log 706 are transferred, through the external device,to a remote server 602, database 604, workstation 610, programmer 606 orotherwise.

By maintaining the confirmation log, for a particular CA data set at thelocal external device in association with the original CA data set,remote devices (e.g., programmer 606, server 602, etc.) receive andprocess both the original CA data set and the confirmation log. Theremote device obtains the “traditional” device diagnostic sections, andis also afforded additional information from the confirmation log and isable to account (at 718) for cumulative adjustments/adjudications in AFdetection before displaying a consolidated set of AF statistics anddiagnostics to a physician or medical personnel.

Additionally, or alternatively, the operations of FIG. 7 may beimplemented in connection with remotely stored confirmation logs, suchas in communication sessions 720. At 722, a remote device may request CAdata from a particular ICM by conveying a corresponding request to alocal external device associated with the corresponding ICM. The localexternal device forwards the data request, at 724, to the ICM, inresponse thereto, at 726, the ICM transmits the CA data set to the localexternal device. The local external device forwards the CA data set, at728, to the remote device. Optionally, before relaying the CA data set,at 728, the local external device may first determine whether the CAdata set has first been analyzed for AF detection confirmation. In theexample at 720, it is presumed that the CA data set has already beenanalyzed for AF detection confirmation and thus the local externaldevice need not perform the confirmation analysis at this time.Additionally, or alternatively, the remote device may include, in therequest, a direction to the local external device to not perform AFdetection confirmation (e.g., the remote device knows that in AFdetection confirmation has already been performed and stored elsewhere).

In connection with or separate from the request for CA data set at 722,the remote device conveys a request, at 730, to a server and databasefor any confirmation logs related to the requested CA data set. Therequested may be broadcast to multiple external devices on the networkor directed to a particular server/database known to maintaininformation in connection with the particular ICM. Additionally, oralternatively, the remote device may hold the request, at 730, untilafter receiving the CA data set, at 728. For example, once a remotedevice receives the CA data set, at 728, the remote device may include,within the request for confirmation logs, an indication of the time anddate at which the CA data set was collected. In response to the request,the server and database return, at 732, one or more confirmation logs(if present). Thereafter, the remote device combines the CA data set andconfirmation log 706 to present a consolidated summary of the data to aphysician or other medical personnel.

In connection with embodiments herein, the cloud-based approach allowsan AF episode that is detected by the ICM using the traditionaldetection algorithms, to be passed through the local external device andstored at the server 602, database 604, workstation 610 or at anotherremote device within the cloud-based system. When an individual ICM isinterrogated for a CA data set, the interrogation device would alsorequest, from the cloud-based system, any additional information, suchas any confirmation logs stored elsewhere within the system. Forexample, when an external device, such as a cell phone 614, localmonitoring device 608, 616 and/or programmer 606 interrogate anindividual ICM, the cell phone 614, local monitoring device 608, 616and/or programmer 606 would also broadcast an ICM data supplementrequest over the cloud-based system. The ICM data supplement requestrequests additional data/information related to the individual ICM(e.g., based on the ICM serial number). In response thereto, the server602 and/or other remote system may provide, to the requesting device,one or more confirmation logs or other information regarding pastoperation of the ICM. The requesting device then combines the CA dataset from the ICM with related data (e.g., a confirmation log associatedwith a particular AF episode and/or group of cardiac events) from anexternal source. The external devices pulls data from the cloud inconnection with ICM interrogation, and combine the CA data from the ICMwith any corrective or confirmation data from the log, before presentinga consolidated data summary to a physician or medical personnel.

Next, alternative embodiments are described for detecting R-waves inconnection with a first pass or second pass arrhythmia detectionprocess. The R-wave detection processes of FIGS. 9 and 10 may beimplemented by firmware on an ICM, IMD, local external device or remoteserver, as a first pass process in connection with arrhythmia detection.Additionally, or alternatively, the R-wave detection processes of FIGS.9 and 10 may be implemented by firmware on an ICM, IMD, local externaldevice or remote server, as a second pass confirmation process, wherecardiac activity signals have been previously analyzed by an AFdetection module, such as the ORI process described in connection withFIGS. 2A and 2B. The process may initiate the operations of FIGS. 9 and10 in an attempt to verify whether one or more episodes in a CA dataset, are in fact an AF episode or a normal rhythmic/sinus episode.Optionally, the operations of FIGS. 9 and 10 may be implemented inconnection with a CA data set that has not been previously analyzed forpotential AF episodes. The operations of FIGS. 9 and 10 may beimplemented as part of a local or distributed system, such as by themicrocontroller 121 of the ICM, by a local external device and/or aremote server.

III. ALTERNATIVE EMBODIMENT—IMPROVED R-WAVE DETECTIONALGORITHM—BRADYCARDIA AND ASYSTOLE EPISODES USING A SECOND PASSDETECTION WORKFLOW

FIG. 8A illustrates a block diagram of parallel signal processing pathsimplemented in accordance with embodiments herein. In FIG. 8A, the ICM100 collects CA signals for a series of cardiac events or beats throughfar field sensing by two or more electrodes 114, 126 on or proximate tothe housing of the ICM 100. By way of example, the ICM 100 may performsensing using wide frequency bandpass filter to collect EGM (or VEGM)signal that contains P, QRS, and/or T-waves. The VEGM signal isprocessed along a primary sensing channel (or first pass) 801 and asecondary/confirmation sensing channel (or second pass) 803. In theprimary sensing channel 801, sensed cardiac activity signals (e.g., VEGMsignals) are passed through a hardware filtering circuit 805 to form afiltered cardiac activity signal VSENSE 807. The VSENSE signal 807 isanalyzed by an onboard arrhythmia detection process within the ICM,generally referred to as an onboard RR interval irregularity (ORI)process 809. The ORI process 809 identifies R-waves and arrhythmiaepisodes using the VSENSE signal 807, stores binning information inconnection with, and designates device documented markers, such asR-wave markers and arrhythmia markers, that are temporally aligned withthe VSENSE signal 807.

In addition, the VEGM signal 811 is processed along thesecondary/confirmation sensing channel 803, wherein the VEGM signal 811is directly analyzed by a second pass detection algorithm 813 asdescribed herein. In accordance with embodiments herein, methods andsystems are described that utilize the second pass detection algorithm813 to improve physiologic signal sensing (e.g., R-waves) in connectionwith arrhythmia episodes (e.g., bradycardia, tachycardia and asystole).The detection algorithm 813 applies a feature enhancement process 815 tothe secondary sensed signal (e.g., the VEGM signal) to form a modifiedsecondary CA signal. The modified secondary CA signal includes enhancedfeatures of interest (e.g., QRS complex and R wave peak) and suppressedfeatures that are not of interest (e.g., P-wave, T-wave, baselinenoise). The feature enhancement process 815 includes signalconditioning, noise reduction and peak sensing. The detection algorithm813 also includes a feature under-detection process 817. The featureunder-detection process 817 analyzes the modified cardiac activitysignal for under detected features of interest. While analyzing themodified cardiac activity signal, the process 817 automatically adjustsan adaptive sensitivity threshold that is applied to the modified CAsignal. The adaptive sensitivity threshold changes between events and/orepisodes. The detection algorithm 813 provides a reduction in falsedeclaration of bradycardia and asystole episodes by the ORI process 809,while maintaining the sensitivity in detecting true bradycardia andasystole episodes. In particular, the detection algorithm 813 provides ahigh level of sensitivity in connection with bradycardia and asystoleepisodes due, in part to, (1) using signal characteristics in the VEGMsignal in the interval prior to these episodes to select a moreappropriate threshold of sensing and (2) incremental adjustment of theadaptive sensitivity level until an R-wave is detected or a calculatedlowest allowed sensitivity limit is reached.

In the example of FIG. 8A, the secondary signal represents a sensedsignal such as a VEGM signal 811. Optionally, the secondary signal mayrepresent a sensed signal derived by mathematical transformation. Thedetection algorithm 813 affords a computationally simple process thatmay be implemented on board the ICM, such as at the firmware level.Optionally, the detection algorithm 813 may be implemented at a mid-warelevel, such as on a local external device (e.g., cell phone, tabletdevice, Merlin@Home™ transmitter) that communicates with the implanteddevice, and/or at a software level such as on a remote monitoring server(e.g., the Merlin.net™ Patient care network, a device data translator).

FIG. 8B illustrates a portion of rectified CA signal processed by thesecond pass detection/confirmation algorithm 813 of FIG. 8A. Therectified CA signal 820 illustrates QRS complexes for cardiac events orbeats 821-823, each of which includes QRS complex that is rectified toinclude three positive peaks. In the example of FIG. 8B, the QRS complexof beat 823 includes rectified local peaks 824-826. FIG. 8B alsoillustrates a dashed line which corresponds to a sensitivity level 828utilized by the ICM 100 (FIG. 8A). The adaptive sensitivity limit 830 isthe lowest sensitivity level allowed for detecting a QRS complex. When aQRS complex, such as in beat 823, exceeds the sensitivity level 828, theICM 100 declares an R-wave. Alternatively, when the QRS complex does notexceed the sensitivity level 828, such as in beat 822, the ICM 100 doesnot declare an R-wave and the beat 822 goes under detected, therebyresulting in the ICM 100 improperly annotating beat 823 as an abnormalor bradycardia beat due to the long interval from previously sensed beat821

In accordance with embodiments herein, the second passdetection/confirmation algorithm 813 (as described in connection withFIG. 9A) sets the adaptive sensitivity limit 830 at a level havinggreater sensitivity relative to the sensitivity of conventional ORIprocess 809. Consequently, the R-wave peak of beats 821, 822, 823 areproperly detected and properly classified.

FIG. 9A illustrates a process for detecting bradycardia and asystoleepisodes implements by the second pass detection algorithm 813 inaccordance with embodiments herein. At 902, one or more processorsdetermine that the ICM device documented bradycardia episode or asystoleepisode based on cardiac activity signals detected in the primarysensing channel 801. For example, the ORI process 809 applies an R-wavesensing and RR interval based bradycardia and asystole detectionalgorithm implemented by the ICM. When an RR interval exceedsbradycardia and/or asystole detection cutoff limits the ICM declares abradycardia episode and/or an asystole episode.

Optionally, the operations at 904-910 perform preconditioning andfeature enhancement upon the original CA signal received over thesecondary sensing channel 803 to, among other things, improve asignal-to-noise ratio for the feature of interest (e.g., the peak of theR-wave) in the CA signal. For example, the operations at 904-910 may beimplemented by the feature enhancement process 815 (FIG. 8A). At 904,the one or more processors resample the CA signal (e.g., utilizinginterpolation) to increase a resolution of the data samples within theCA data set for the CA signal. Resampling the CA signals allowsrelatively more precise settings of thresholds and more detailed sensingoperations to be performed at later operations in FIG. 9A. By way ofexample, the original CA signals (e.g., VEGM signals) may be defined bya CA data set that has a sample frequency of 128 Hz, whereas theresampling and interpolation increase the sample resolution to 512 Hz.Various types of interpolation may be applied, such as linearinterpolation or Shannon interpolation, in which zeros are added betweenpoints and the signal is digitally low-pass filtered and multiplied bythe reciprocal of the up-sampled ratio. Alternative techniques may beapplied to increase the data sample resolution.

The interpolation and resampling operation at 904 may be desirable whenthe CA data set is stored at relatively low resolution. For example,while the ICM may digitize sensed signals at a higher resolution andinitially analyze the digitized signals at the higher resolution, theICM may not include sufficient memory to store all of the data for theCA signal at the higher digital resolution. Consequently, the resolutionof the CA signal may be reduced before the digital data is stored inmemory. As another example, the resolution of the data may be reduced inconnection with transmission from the ICM. For example, beforetransmitting a CA data set, the ICM may down sample the digitized data,in order to maintain a desired data rate and/or to conserve power byreducing the overall data to be transmitted.

Optionally, when the CA data set is stored with sufficient resolutionand/or transmitted from the ICM with sufficient resolution, theresampling and interpolation operation at 904 may be omitted entirely.

At 906, a baseline drift reduction operation is performed. The one ormore processors may step through the data samples and subtract a movingaverage from the CA signal. For example, a long (e.g., one second)moving average of the CA signal may be subtracted from the CA signal ateach data point. Additionally, or alternatively, the drift reductionoperation may be performed by applying a high pass digital filter havingcorresponding desired filter characteristics.

At 908, a baseline noise reduction operation is performed. For example,the one or more processors may step through the data samples of the CAsignal and apply a moving average at each data sample. For example, themoving average window, having a predetermined length (e.g., 10 msec.),may be applied to replace each data point along the CA signal with anaverage of the data points surrounding the current data point within themoving average.

At 910, the one or more processors apply a feature enhancement functionalong the CA signal to form an enhanced feature of interest (e.g., apeak of the R-wave). The feature enhancement function also suppressesfeatures that are not of interest. By way of example, the one or moreprocessors may apply non-linear scaling function (e.g., an amplitudesquaring) and normalization function along the CA signal to enlargeand/or sharpen peaks of the R-wave. For example, the normalization maybe achieved by dividing each data point along the CA signal by apercentage of a peak of the R-wave (e.g., data point 1 divided by 80% ofthe R-wave peak). By normalizing the CA signal to a value less than thepeak of the feature of interest, the normalization will enhance R-wavepeaks, while suppressing non-R-wave features such as P-waves, T-waves,noise and other features that are not of interest. The non-linearscaling operation has a similar effect by enhancing the R-wave peaks andsuppressing non-R-wave features.

Next, the process of FIG. 9A begins a search through the CA data set forbeat segments of interest suspected of under-sensing. As noted herein,the CA data set includes a marker channel that includes a series ofdevice documented markers temporally associated with features ofinterest from one or more CA signal channels that include correspondingCA signals. The device documented (DD) markers are declared by the ORIprocess 809 in real-time. At 912, the one or more processors identifybeat segments of interest from the CA data set based on the markerchannel. For example, the processors may identify the beat segments ofinterest of the CA signal as the segments that correspond to triggeringthe DD arrhythmia markers

At 914, the one or more processors review the marker channel within theCA data set to identify beats that were identified by the ICM (and ORIprocess) to have stable RR intervals. The processors may identify all ora portion of the segments of the CA data set that have stable RRintervals. The processors utilize the markers from the marker channel,associated with the CA signal segments, to identify the feature ofinterest, such as R-waves, P-waves, T-waves, etc. The one or moreprocessors determine amplitudes (e.g., amplitude estimates) for thefeatures of the CA signal segments having stable RR intervals. Forexample, the processors may estimate amplitudes for R-wave peaks, T-wavepeaks, P-wave peaks, isoelectric segments and the like for stable beats.The amplitude estimates can be used to determine an ensemblecharacteristic for each feature (e.g., ensemble P-wave peak amplitude,ensemble R-wave peak amplitude, ensemble R-wave peak amplitudevariability, ensemble T-wave peak amplitude).

FIG. 8C illustrates a portion of a CA data set 840 that is analyzed inconnection with the operation at 914 to identify amplitudes of featuresof interest. In FIG. 8C, the CA data set 840 includes beats or cardiacevents 842-844. The CA data set 840 also includes a marker channel(illustrated superimposed upon the CA signal). The marker channelincludes R-wave markers 845-847 identified in real-time by the ICM inconnection with the corresponding cardiac events 842-844.

During the amplitude estimation operation at 914, the one or moreprocessors use the R-wave markers 845-847 as reference points relativeto each corresponding cardiac event 842-844. The processors definesearch windows 848-850 for a corresponding cardiac event 842, withrespect to an R-wave marker 845. For example, an R-wave search window849 may be defined to be centered at the R-wave marker 845. A P-wavesearch window 848 may be defined to precede the R-wave search window849, while a T-wave search window 850 may be defined to follow theR-wave search window 849. Search windows 848-850 can be constant ordependent on RR intervals or other functions.

Once the P-wave, R-wave and T-wave search windows 848-850 are defined,the one or more processors analyze the CA signal within thecorresponding windows 848-850 to identify a peak therein. In the exampleof FIG. 8C, P-wave, R-wave and T-wave peaks 851-853 are illustrated asdetermined by the foregoing process. The amplitudes of peaks 851-853 arethen utilized to calculate a starting value for the adaptive sensitivitylevel as described hereafter. Multiple beats are analyzed to determineensemble amplitudes for P, R and T-wave peaks.

Returning to FIG. 9A, at 916, the one or more processors utilize theensemble amplitudes of the features of interest to calculate a startingvalue for the adaptive sensitivity level. For example, the startingvalue of the adaptive sensitivity level may be determined based on acomparison of a weighted ensemble R-wave peak, weighted ensemble T-wavepeak and weighted ensemble P-wave peak. The term “ensemble” is used torefer to estimate of the amplitude of a group of wave peaks of interestwithin a current CA data set (e.g., the highest R-wave peak within a 90millisecond strip of EGM signals) or a select segment of the CA dataset. For example, the processors may apply weighting factors andconstant offsets to the amplitude estimates and then select a weightedamplitude estimate having a desired characteristic relative to otherweighted amplitude estimates. As one non-limiting example, theprocessors may determine the lower of R_(ENS-W) and T_(ENS-W), whereR_(ENS-W) represents ensemble R-wave peak multiplied by an R-waveweighting factor (e.g., 50%) plus a constant offset (e.g., 10 mV) andT_(ENS-W) represents an ensemble T-wave peak multiplied by a T-waveweighting factor (e.g., 120%) plus a constant offset (e.g., 10 mV). Thelower of R_(ENS-W) and T_(ENS-W) represents an RT_(min), namely aminimum RT reference level that is greater than the T-wave peak and lessthan the R-wave peak. The processors may then determine the higher ofthe RT_(min) and a P_(ENS-W), where P_(ENS-W) represents an ensembleP-wave peak multiplied by a P-wave weighting factor (e.g., 120%) plus aconstant offset (e.g., 20 mV). The processors determine the higher ofthe RT_(min) and P_(ENS-W) to avoid setting an upper sensitivitythreshold near a noise floor. The higher of the RT_(min) and P_(ENS-W)is used to set the adaptive sensitivity level. For example, the adaptivesensitivity level may be set to equal the higher of the RT_(min) andP_(ENS-W). Optionally, the adaptive sensitivity level may be set to be apercentage of, or a predetermined amount above/below the higher of theRT_(min) and P_(ENS-W).

The sensitivity level may be modified in connection with different beatsegments of interest within a CA data set. For example, a new adaptivesensitivity level may be set for each beat segment of interest or groupsof beat segments of interest. Alternatively, the sensitivity level maybe modified once in connection with each CA data set.

Optionally, the one or more processors may utilize an average peakamplitude of one or more features of interest as the “ensemble”amplitude of the feature of interest. For example, an average peakR-wave amplitude may be determined for all or a subset of the stablebeats within the CA data set, where the average peak R-wave amplitude isused to obtain the weighted ensemble R-wave amplitude. A similaroperation may be implemented for an average P-wave peak amplitude and anaverage T-wave peak amplitude. Optionally, the operation at 912 toidentify one or more beat segments of interest may be reordered to occurafter 914 and/or after 916.

At 918, the one or more processors set a starting adaptive sensitivitylevel based on amplitudes of the features of interest from the precedingbeat and/or based on an ensemble average of features of interest from acollection of preceding beats. FIG. 9B illustrates an example processfor detecting unanalyzable beat segments due to noise in accordance withan embodiment herein.

Additionally, or alternatively, a one-shot adaptive sensitivity levelmay be set to a percentage or portion of the adaptive sensitivity limit,a percentage or portion of a feature of interest, a programmed level andthe like. By way of example, the adaptive sensitivity level may bedetermined based on ensemble of prior R-wave amplitude and ensembleR-wave variability in the segment of interest. For example, when theensemble R-wave peak amplitude is a relatively large and ensemble R-wavevariability is low, the adaptive sensitivity level may be assignedweighted ensemble of R-wave peak amplitude (e.g., 50% of prior R-wave).Alternatively, when the ensemble R-wave peak amplitude is medium andensemble R-wave variability is medium, the adaptive sensitivity levelmay be assigned a slightly small weighted ensemble of R-wave peakamplitude (e.g., 30% of prior R-wave). As another example, when theensemble R-wave peak amplitude is small or ensemble R-wave variabilityis above medium, the adaptive sensitivity level may be assigned aweighted combination of T-wave peak amplitude ensemble and/or weightedcombination of P-wave peak amplitude ensemble plus an offset (e.g., theminimum of 1.1*T-wave ensemble peak amplitude+0.10 mV and 1.2*P-waveensemble+0.020 mV). The low, intermediate and high levels of variabilitymay be predetermined and/or automatically calculated by the ICM overtime.

At 922, the one or more processors apply the current sensitivity levelto perform an R-wave detection process to the current segment ofinterest beat. For example, the processors analyze the CA signal segmentto search for features of interest, such as P-waves, R-waves andT-waves. At 922-928, the one or more processors automaticallyiteratively analyze a current beat segment of interest. At 922, the oneor more processors determine whether one or more R-waves are presentwithin the beat segment of interest. For example, the processors maycompare the CA signal to the current adaptive sensitivity level in thebeat segment of interest. The processors may determine whether one ormore peaks of the current beat segment of interest exceed the currentadaptive sensitivity level. When the one or more peaks of the beatsegment of interest, exceed the current adaptive sensitivity level, theprocess determines that the beat segment of interest includes an R-wave.In accordance with an embodiment, the processors identify the point inthe beat segment of interest that exceeds the current adaptivesensitivity level as R-wave.

Additionally, or alternatively, the processors may identify local peakswithin the beat segment of interest and compare the peaks to identify alargest one of the local peaks as the peak of the R-wave. For example,the processors may search the beat segment of interest for a feature ofinterest, such as a peak of R-wave.

When an R-wave is detected at 922, flow moves to 924. At 924, the one ormore processors determine whether to repeat the operations in connectionwith a next beat segment(s) of interest. Alternatively, at 922, when theprocessors determined that no R-wave was detected, flow branches to 926.

At 926, the one or more processors determine whether the currentsensitivity level has reached the lowest adaptive sensitivity limit(determined at 916) associated with the current segment or CA data set.When the current sensitivity level reaches the lowest adaptivesensitivity limit, flow moves to 924. At 924, the processors determinewhether to repeat the operations in connection with a next beat segmentof interest. Alternatively, at 926, when the current sensitivity levelhas not yet reached the adaptive sensitivity limit, flow branches to928. At 928, the one or more processors increase the current sensitivitylevel (e.g., by 5%, 10%, 20% or 0.010 mV) and flow returns to 922. Itshould be recognized that by increasing the sensitivity in the contextof sensing R-wave, the processors numerically decreases the value of theadaptive sensitivity level/limit.

In the present example, the lowest adaptive sensitivity limit iscalculated for a segment of the CA data set. Optionally, the lowestadaptive sensitivity limit may be determined once for the entire CA dataset, and not repeatedly calculated in connection with each beat segmentof interest and/or groups of beat segment of interests.

In the foregoing manner, the operations at 914-928 repeat the iterativeanalysis at 922 while progressively adjusting the current adaptivesensitivity level until i) one or more R-waves or CA signal are detectedin the beat segment of interest and/or ii) the current sensitivity levelreaches the lowest adaptive sensitivity limit. When an R-wave or CAsignal is detected within the current beat segment or the sensitivitylimit is reached, flow passes to 924 to determine whether additionalbeat segments of interest exist. When additional beat segments exist,the process returns to 914 and a new starting adaptive sensitivitythreshold is determined for the next beat segment of interest.Otherwise, the process moves to 930. Alternatively, a one-time R-waveadaptive sensitivity level using step 922 can be performed without stepsof 926 and 928 to reduce the calculation burden introduced by theiterative analysis.

At 930, the one or more processors analyze the beat segment of interestto detect whether an arrhythmia is present. The detection of thearrhythmia is based at least in part on a presence or absence of one ormore R-waves within the beat segment of interest. For example, theprocessors may confirm or deny the presence of a bradycardia episodeand/or in asystole episode within one or more beat segment of interests.In connection with bradycardia episodes, the processors may maintain arunning count of a number of beats having RR intervals that aresufficiently long (exceed a bradycardia RR interval threshold) to beindicative of a bradycardia episode. The processors may also maintain arunning count of a total number of beats being analyzed from the CA dataset. A bradycardia episode may be confirmed or denied based on a numberof X beats within a bradycardia zone out of a number of Y total beatsover all or a portion of the CA data set. For example, the one or moreprocessors may maintain a bradycardia bin counting a number of beatsthat exhibit an RR interval within a bradycardia zone. The processorsmay also maintain a bin counting a total number of beats. When thebradycardia bin count, relative to the total beat bin count, exceeds athreshold, the processors declare a bradycardia episode.

At 930, in connection with confirming or denying an asystole episodewithin one or more beat segment of interests, the one or more processorsmay track a time period (e.g., greater than X seconds) during which nodetected activity-waves occurs within the beat segment of interest. Whenthe period of time, with no detected electrical activity, exceeds anasystole threshold, the processors declare an asystole episode.

Optionally, an additional iterative stage may be applied in connectionwith bradycardia sensing, wherein incrementally lower new sensitivitylevels (e.g., greater sensitivity) are applied when calculating thecurrent adaptive sensitivity level at 918. The new sensitivity levelsmay be incremented based on other continuing/stopping criteria in amanner to rule out bradycardia incrementally as the sensitivity level islowered while decreasing a chance of over sensing T-waves.

The process described in connection with FIG. 9A is computationallyinexpensive as it uses simple operations (such as comparisons,additions, multiplications and moving averages). Therefore, the processof FIG. 9A can be implemented efficiently in a power-constrainedplatform and thus may be implement on multiple platforms (e.g., ICMdevice, local external device or cloud server). Also, performance on thevarious multiple platforms could be mirrored with a high degree ofagreement. By way of example, a limited number of control parameters maybe designed and tested to be hard-coded or self-learned from EGM signalstrips without user input.

The process of FIG. 9A uses an existing CA signal recorded but adds anability to dynamically adjust sensing thresholds based on features ofthe CA signal, such as R, T and P-wave amplitudes and amplitudevariability. The process of FIG. 9A also uses iterative processing tosearch for under-sensed bradycardia beats by lowering the sensingthreshold up to the adaptive sensitivity limit.

A performance of the process of FIG. 9A was developed/tested on trainingand test data sets of 6541 and 996 episodes collected from fielddevices. The process of FIG. 9A exhibited an improvement of positivepredictive value to >99% and >88%, improvement of relativesensitivity >99% and >96% and reduction of false detection rate of >99%and >95% in bradycardia and asystole, respectively, relative toconventional ORI processes.

IV. ALTERNATIVE EMBODIMENT—R-WAVE DETECTION USING SELF-ADJUSTINGPARAMETERS AND PHYSIOLOGIC DISCRIMINATORS (1^(ST) & 2^(ND) PASS)

Today, conventional ORI processes are programmed at the time of implantor manufacture, with the parameters for the ORI process remaining fixedthroughout the useful life of the ICM. In conventional ORI processes,the ORI parameters are not automatically re-programmable and are noteasily reprogrammed by a clinician. Opportunities remain to improve uponconventional ORI processes.

While the conventional ORI process provides high accuracy to senseintra-cardiac R-wave signals, an opportunity remains to improve upon theconventional ORI process. For example, an opportunity remains to improveupon the conventional algorithm in a manner that is less dependent onvariations in skin contact with the sensing electrodes. As the interfacevaries between the sensing electrodes and the patient's subcutaneoustissue, the variation may influence the CA signal. In addition, thesensed CA signal may be affected by the nature of far field sensingand/or posture changes. In practice, the conventional ORI processexhibits good sensitivity with positive predictive value. However, anopportunity remains to reduce a rate of false positive detections ofbradycardia and asystole episodes, thereby reducing the amount of falsepositive detections to be reviewed at the clinic.

In accordance with embodiments herein, systems and methods are describedto improve detection of features of interest from a CA signal (e.g.,R-wave peak) by performing a sequence of processing steps, while usingfeatures of the CA signal to calibrate sensitivity parameters of theR-wave detection process. Embodiments herein render the sensitivityprofile parameters of the R-wave detection process adaptive, such thatas the noise level changes, the parameters are adapted. For example, thesensitivity profile parameters may adjust how high or how low initialsensitivity levels are set, adjust the sensitivity limit and the like.In addition, various physiologic/noise discriminators are utilized tofurther handle unmitigated noise and under sensed events. In addition,adaptive sensitivity parameter settings are provided to self-adjustthresholds for varying signals and to perform R-wave peak sensing.

In accordance with embodiments herein, a computer implemented method isprovided for detecting arrhythmias in cardiac activity. The methodcomprises, under control of one or more processors configured withspecific executable instructions, obtaining a far field cardiac activity(CA) data set that includes far field CA signals for beats; identifyinga T-wave characteristic of interest (COI) and an R-wave COI from the CAsignals; adjusting profile parameters of a sensitivity profile based onthe T-wave COI and R-wave COI, the sensitivity profile defining atime-varying sensitivity level and a sensitivity limit; automaticallyiteratively analyzing a beat segment of interest by: comparing the beatsegment of interest to the time-varying sensitivity level to determinewhether an R-wave is present within the beat segment of interest; anddetecting an arrhythmia within the beat segment of interest based on apresence or absence of the R wave; and recording results of thedetecting of the arrhythmia.

Additionally, or alternatively, the identifying and adjusting operationsare performed on a beat by beat basis. Additionally, or alternatively,the method further comprises identifying, in connection with the R-waveCOI, a rise rate of a current beat and determining whether the rise rateof the current beat exceeds a rise rate of a preceding beat by more thana R-wave rise rate threshold. Additionally, or alternatively, the methodfurther comprises adjusting at least one of a start sensitivityparameter that defines a start sensitivity of the sensitivity profile.Additionally, or alternatively, the method further comprisesidentifying, in connection with the T-wave COI and R-wave COI, at leastone of a rapid rise characteristic, a rapid heart rate characteristic,T/R-wave ratio characteristic or a T-wave-to-refractory proximitycharacteristic. Additionally, or alternatively, the method furthercomprises adjusting, based on the T-wave COI and R-wave COI, at leastone of a refractory period duration, decay delay period, startsensitivity, decay rate, or sensitivity limit parameter, the startsensitivity parameter defining a start sensitivity of the sensitivityprofile, the refractory period duration parameter defining a blankinginterval, a decay rate parameter defining a slope of a lineartime-varying sensitivity level decline, the sensitivity limit parameterdefining a lowest sensitivity level that linear sensitivity decline isnot allowed to go below. Additionally, or alternatively, theidentifying, adjusting and analyzing operations are performed, by atleast one of a local external device and a remote server, as a secondpass confirmation for an arrhythmia episode declared by a first passarrhythmia detection algorithm implemented by an implantable device.Additionally, or alternatively, the CA data set includes devicedocumented markers in combination with the CA signals, the CA data setgenerated by the implantable device in connection with the first passarrhythmia detection algorithm, the first pass arrhythmia detectionalgorithm declaring the arrhythmia episode to be one of a bradycardia,tachycardia, asystole or atrial fibrillation episode.

Additionally, or alternatively, the method further comprises adjustingthe sensitivity limit based on amplitude of the CA signal during priornon-ventricular segments. Additionally, or alternatively, the methodfurther comprises applying a feature enhancement function to the CAsignals to form an enhanced feature in the CA data set, wherein theapplying the feature enhancement function enhances at least one of anR-wave feature, T-wave feature, P-wave feature and suppresses noise.Additionally, or alternatively, the method further comprises overlayinga search window onto a current beat segment of the CA signals anddetermining whether the current beat segment contains noise signature,and based thereon retaining or removing the current beat segment fromthe CA signals. Additionally, or alternatively, the method furthercomprises determining a signal to noise ratio (SNR) for the current beatsegment, comparing the SNR with a threshold that corresponds to anon-physiologically high noise level, the identifying comprisinglabeling the current beat segment contains noise signature based on thecomparison of the SNR to a threshold. Additionally, or alternatively,the method further comprises determining an energy content of thecurrent beat segment in the search window, comparing the energy contentwith a threshold range that corresponds to a non-physiologically highnoise level, the identifying comprising labeling the current beatsegment to represent a beat segment contains noise signature based onthe comparison of the energy content to a threshold. Additionally, oralternatively, the method further comprises determining a derivative ofthe current beat segment in the search window, comparing the derivativewith a threshold that corresponds to a non-physiologically high noiselevel, the identifying comprising labeling the current beat segment torepresent a beat segment contains noise signature based on thecomparison of the derivative to a threshold. Additionally, oralternatively, the method further comprises identifying a current beatsegment that has a sensitivity level of interest within the CA signals;labeling the current beat segment as a beat segment of interest;identifying a sinus segment located proximate to the beat segment ofinterest; comparing the sinus and beat segment of interest; anddeclaring the beat segment of interest to represent an under sensedsegment, asystole segment or bradycardia segment based on the comparingoperation.

In accordance with embodiments herein, a system is provided fordetecting arrhythmias in cardiac activity. The system comprises: memoryto store specific executable instructions; and one or more processorsconfigured to execute the specific executable instructions for:obtaining a far field cardiac activity (CA) data set that includes farfield CA signals for beats; identifying a T-wave characteristic ofinterest (COI) and an R-wave COI from the CA signals; adjusting profileparameters of a sensitivity profile based on the T-wave COI and R-waveCOI, the sensitivity profile defining a time-varying sensitivity leveland a sensitivity limit; automatically iteratively analyzing a beatsegment of interest by: comparing the beat segment of interest to thetime-varying sensitivity level to determine whether an R-wave isdetected within the beat segment of interest; and detecting anarrhythmia within the beat segment of interest based on a presence orabsence of the R-wave; and recording results of the detecting of thearrhythmia.

Additionally, or alternatively, the processor is configured to performthe identifying and adjusting operations on a beat by beat basis.Additionally, or alternatively, the processor is further configured toidentify, in connection with the R-wave COI, a rise rate of a currentbeat and determining whether the rise rate of the current beat exceeds arise rate of a preceding beat by more than a R-wave rise rate threshold.Additionally, or alternatively, the processor is further configured toadjust at least one of a start sensitivity parameter that defines astart sensitivity of the sensitivity profile. Additionally, oralternatively, the processor is further configured to identify, inconnection with the T-wave COI and R-wave COI, at least one of a rapidrise characteristic, a rapid heart rate characteristic, T/R-wave ratiocharacteristic or a T-wave-to-refractory proximity characteristic.Additionally, or alternatively, the processor is further configured toadjust, based on the T-wave COI and R-wave COI, at least one of arefractory period duration, start sensitivity, decay rate, orsensitivity limit parameter, the start sensitivity parameter defining astart sensitivity of the sensitivity profile, the refractory periodduration parameter defining a blanking interval, a decay rate parameterdefining a slope of a linear time-varying sensitivity level decline, thesensitivity limit parameter defining a lowest sensitivity level thatlinear sensitivity level decline is not allowed to go below.Additionally, or alternatively, the processor is further configured toconfirm or deny at least one of a bradycardia episode based on a numberof X beats within a bradycardia zone out of a total of Y beats withinthe CA data set. Additionally, or alternatively, the processor isfurther configured to confirm or deny an asystole episode when the beatsegment of interest exhibits no detected electrical activity for aperiod of time that exceeds an asystole threshold. Additionally, oralternatively, the system further comprises an implantable medicaldevice housing the processor and memory. Additionally, or alternatively,the processor and memory are housed within at least one of a localexternal device and a remote server. Additionally, or alternatively, theprocessor is further configured to adjust the sensitivity limit based onamplitude of the CA signals during prior non-ventricular, segments.

As one non-limiting example, the processes described in connection withFIGS. 9B and 9C have been modeled with respect to the conventional ORIprocess (described in connection with FIG. 8B) that utilizes fixedparameters to define automatic sensing control (ASC). The internalmodelling utilized a random dataset of field events that included atotal of 996 EGM signal data strips, each of which included a 30 secondrecording of cardiac activity recorded by a commercially released ICM.The processes of FIGS. 9B and 9C achieved a reduction of false eventsby >84%, as compared to a convention ORI detection process. Also theprocesses of FIGS. 9B and 9C herein maintained a >93% relativesensitivity, as compared to the conventional ORI detection process, forboth bradycardia and asystole episodes.

FIG. 9B illustrates a process for R-wave detection in accordance withembodiments herein. The process of FIG. 9B will be described inconnection with FIG. 10B. FIG. 10B illustrates examples of signalsproduced at the various operations within FIG. 9B. The operations at948-952 apply a feature enhancement function to the CA signals to formenhanced R-wave features in the CA data set.

At 948, the one or more processors bandpass filter and rectify the CAsignals. The CA signals may be obtained from various sensing channels ofthe ICM. For example, the CA signals may be collected over the VEGMsensing channel and/or the VSENSE sensing channel. The bandpass filtermay be implemented as a hardware or software filter that is configuredwith desired passband coefficients (e.g., passband 8 to 35 Hz). Forexample, the bandpass filter may represent a digital finite impulseresponse filter. The bandpass filter is applied to the CA signals tosuppress frequency bands that do not contribute to a feature ofinterest. For example, the bandpass filter may be configured to maintainpeaks of the R-wave, while suppressing lower-frequency components (e.g.,T-waves, P-waves and baseline isoelectric) and suppressinghigh-frequency components (e.g., myopotential noise). At 948, the one ormore processors also apply a hardware or software rectifier to thebandpass filtered signal in order to change signal components ofnegative polarity to positive polarity to form a modified CA signal thatrepresents a rectified bandpass filtered CA signal. The rectifiedband-pass filtered CA signal splits the energy of the signal intomultiple side peaks with smaller amplitudes relative to the original CAsignal.

In FIG. 10B, a wide band CA signal 1020 is illustrated as an examplesegment for one cardiac beat. The wide band CA signal 1020 is filteredto provide a narrow band CA signal 1021 and then rectified to form arectified bandpass filtered CA signal 1022.

Returning to FIG. 9B, at 949, the one or more processors apply a featureenhancement function to the CA signals to form an enhanced feature ofinterest in the CA data set. For example, the processors may firstcompare the modified CA signal to a lower boundary threshold (e.g., 1unit of an analog to digital converter). Segments of the modified CAsignal that fall below the lower boundary threshold represent noise,while segments of the modified CA signal that exceed the lower boundarythreshold may include “potential” or “candidate” features of interest.The one or more processors may apply a mathematical enhancement functionto the segments of the CA signal that exceed the lower boundarythreshold. For example, the processors may apply a square function tothe segments of the CA signal that exceed the lower boundary threshold,in order to increase separation between peaks and non-peaks in the CAsignals. The operation at 949 generates an “enhanced” modified CA signalthat includes an enhanced feature of interest (e.g., enhanced R-wavefeature). As shown in FIG. 10B, the operation at 949 converts therectified CA signal 1022 to a squared CA signal 1023.

At 950, the one or more processors apply a further feature enhancementfunction to the CA signal, namely a moving filter to successive windowsof the modified “squared” CA signal to further enhance the FOI. Thewindow width and the filter coefficients may be adjusted/tuned based ona duration of previous peaks identified in prior segments of the currentor a prior CA data set. For example, when the operations of FIG. 9Boperate upon a 30 second segment of the CA data set, the window widthand filter coefficients may be tuned based on a prior CA data set for adifferent 30 second segment of CA signals. Additionally, oralternatively, the window width and filter coefficients for a secondsegment of the CA signal (e.g., a middle or later 5 second segment) maybe tuned based on a first or prior segment of CA signals (e.g., a firstor earlier 5 second segment). The window width and filter coefficientsare defined such that the peak (R-wave) is further enhanced apart fromnon-R-wave peak features. For example, a QRS segment, having a durationof 80 msec, may be copied a select number of times (e.g., copied 2times) and aligned with one another in a temporally shifted manner. Forexample a first copy of the 80 msec CA signal segment may be shifted −80msec with respect to the original 80 msec CA signal segment, while theoriginal CA signal segment is maintained with a 0 msec shift, and asecond copy of the CA signal segment may be shifted +20 msec withrespect to the original CA segment. The copies and original CA signalsegments are constructively added to produce higher separation betweenthe R-wave peak and a remainder of the signal content of the CA segment.As shown in FIG. 10B, the shifted signal 1024 is formed and thenconstructively added to form a constructive overlapping CA signal 1025.

The foregoing signal conditioning operations at 948-950 provide afeature enhancement to the CA signals to form enhanced features ofinterest with an increased signal-to-noise ratio. In particular, thefeature enhancement increases the separation between the R-wave peak andthe peaks of the T-wave, P-wave and isoelectric segment. Additionally,or alternatively, the operations at 948-950 may correlate the CA signalsegment with an M-shaped template similar in shape to the rectifiedsignal. The operations at 948-950 provide constructive coherence toamplify the divided R-peak while leaving the dome shaped T-waves andP-waves around the same amplitude as in the original CA signal.

Optionally, at 951, the one or more processors may determine whether tocombine a select combination of the CA signals processed in differentmanners to form modified CA signals. When signals are to be combined,flow moves to 952, otherwise flow continues to 953. At 952, the one ormore processors form a combination (e.g., by addition) of wide-band,narrow band, rectified and/or derived signals (e.g., by differentiationor integration operators) to form the modified CA signals. Variouscombinations of the signals may be combined in order to produce largerseparation between features of interest and background signals, thusincreasing the signal-to-noise ratio. The modified CA signal (from 951or from 952) is then utilized for analysis for physiologic/noisediscrimination as discussed hereafter.

In accordance with the foregoing, the operations at 948-952 apply afeature enhancement function to the CA signals to form an enhancedfeature of interest in the CA data set.

Next, the operations at 953-958 identify one or more beat segment ofinterests within the CA data set. At 953-956, the one or more processorsapply physiologic/noise discrimination on successive beat segments. Thebeat segments may overlap or extend contiguous with one another. At 953,the one or more processors overlay a search window over the modified CAsignal, where the search window has a duration corresponding to amulti-beat segment. The length of the search window (and multi-beatsegment) may vary, provided that the search window encompass a desirednumber of beats sufficient to enable the process to characterize thenumber of beats as physiologic or non-physiologic. Next the process maybranch along one or more of multiple alternative or parallel paths thatapply various criteria to determine whether the beats within the searchwindow represent a stable or beat segment of interest.

At 954C, the one or more processors determine a signal to noise (SNR)ratio for the CA signal segments in the search window. At 955C, the oneor more processors compare the SNR ratio for the CA signal for thecurrent beat segment within the search window with a threshold that isselected to correspond to a non-physiologically high noise level. Thethreshold may be defined in various manners. As one example, thethreshold may define a percentage of a dynamic range of the CA signals.For example, the noise should not exceed 15%, 25%, etc. of the dynamicrange for the current beat segment within the search window.Alternatively, at least 75%, 85%, etc. of the signal content for thecurrent beat segment within the CA signals should be classified as“signal”, not noise. At 955C, if the SNR is below the threshold, flowbranches to 956 as the process deems the signal quality unsuitable, andthus labels the current beat segment to represent an beat segment ofinterest. Otherwise, at 955C, if the SNR is above the threshold, theprocess deems the signal quality suitable, and thus labels the currentbeat segment to represent a stable beat segment and flow branches to957.

Additionally, or alternatively, flow branches from 953 to 954B. At 954B,the one or more processors determine energy content or integral for theCA signals of the current beat segment within the search window. At955B, the one or more processors compare the energy content (orintegral) for the CA signals for the current beat segment with energythreshold that is selected to correspond to a non-physiologically highenergy level. The energy threshold may be defined in various manners. At955B, if the energy content is above the energy threshold, flow branchesto 956 as the process deems the signal quality unsuitable and the beatsegment un-analyzable. Otherwise, at 955B, if the energy content isbelow the physiological energy threshold, the process deems the signalquality suitable, and includes beat segment in analysis and flowbranches to 957.

Additionally, or alternatively, flow branches from 953 to 954A. At 954A,the one or more processors determine a maximum slope or derivative forthe CA signal of the current beat segment within the search window. At955A, the one or more processors compare the maximum slope or derivativefor the CA signal segments in the current beat segment with a derivativerange. The derivative threshold may be defined in various manners. Thederivative threshold may be an upper threshold and/or a lower thresholdthat define non-physiologically high and/or low derivatives for the CAsignal. For example, a non-physiologically high derivative may occur dueto motion artifacts, or a non-physiologically low derivative may occurfor an extended period of time, such as due to partial or complete lossof skin contact at one or more electrodes. At 955A, if the maximumderivative of the CA signal segment is above or below the derivativethreshold(s), flow branches to 956 as the process deems the signalquality unsuitable, and the beat segment un-analyzable. Otherwise, at955A, if the maximum derivative is within the derivative threshold, theprocess deems the signal quality suitable, and thus labels the currentbeat segment to represent a stable beat segment and flow branches to957. Additionally, or alternatively, the operations at 954A, 955A mayanalyze other aspects of the CA signals, such as the average maximumderivative between positive and negative CA signal peaks, a number ofslope changes in the CA signals, sum of absolute slopes of the CA signalin an interval and the like.

The decisions at 955C, 955B and 955A may be independently used todetermine whether the beats within the current beat segment exhibitnon-physiologic characteristics, thereby representing analyzable beatsegments that should be retained or warrant removal from the modified CAsignals. Optionally, a combination of the decisions from the operationsat 955C, 955B and 955A may be utilized as a physiologic-noisediscriminator to indicate the presence of noise sufficient to deem thesignal quality of the current beat segment unsuitable for further signalanalysis.

When flow advances from one or more of 955C, 955B, 955A and 956, the oneor more processors remove one or more beats of the current beat segmentfrom the modified CA signals to provide a physiologically discriminatedCA signal (also referred to as a physiologically discriminated CA dataset). Optionally, the number of beats or portion of the CA signals thatis removed at 956 may vary based on the determinations at 955C, 955B,and/or 955A. For example, when the SNR is below the threshold (at 955C),but the integral and derivative comparisons are outside the associatedthresholds (at 955B and 955A), then one or a small number of beats maybe removed from the current beat segment at 956. Alternatively, when theSNR, integral and derivative are all outside the correspondingthresholds (at 955C, 955B, 955A), then the entire current beat segmentmay be removed.

At 957, the one or more processors determine whether the analysis hasreached the end of the modified CA signals. If so, the process ends at958. If additional modified CA signals remain to be analyzed, flow movesto 958. At 958, the search window is shifted to a “next” beat segmentwithin the CA signals. The next beat segment may partially overlap theprior beat segment, or be entirely separate from the prior beat segment.The shift at 958 may be a programmed duration (e.g., X msec), aprogrammed number of beats (e.g., 1-9 beats), a full length of thesearch window or another amount.

FIG. 10C illustrates CA signals 1020, 1022 collected in connection witha wide-band (VEGM) sensing channel and a narrow-band (VSENSE) sensingchannel, respectively. The CA signal 1020 for the VEGM sensing channeland/or the CA signal 1022 for the VSENSE sensing channel are analyzedbased on the process of FIG. 9B, and one or more beats of the CA signals1020 and/or 1022 are removed to provide a physiologically discriminatedCA signal and physiologically discriminated CA data set. In FIG. 10C,the beats that are removed are denoted by circular markers or asterisksalong the baseline.

FIG. 9C illustrates a process for confirming or denying a devicedocumented bradycardia or asystole episode in accordance withembodiments herein. False determinations of bradycardia or asystoleepisodes may occur when an ICM experiences variations between high andlow sensitivity levels in connection with sensing CA signals. When thesensitivity varies between high and low levels, the CA signal will alsoexhibit beat segments having similar variations between high and lowsignal amplitudes. When one or more beat segments exhibit a lowsensitivity level, the beat segment is considered to represent a“suspect” beat segment. The process of FIG. 9C utilizes signal integralsand/or signal derivatives to analysis “suspect” beat segments thatpotentially experience under sensing. As explained hereafter, theprocess of FIG. 9C generally compares a “suspect” beat segment, thatpotentially experiences under sensing, with one or more other beatsegments having a desired (e.g., higher) sensitivity level. Thecomparison is utilized to confirm or deny bradycardia and/or asystoleepisodes.

At 960, the one or more processors identify the beat segment that has asensitivity level of interest (e.g., the lowest sensitivity level)within the modified CA signal. The processors label the identified beatsegment as a suspect beat segment. The suspect beat segment may beidentified in various manners. For example, the sensitivity level for abeat segment may correspond to the SNR determined at 954C in connectionwith FIG. 9B for each of the beat segments. When the SNR (determined at954C) is utilized, the processors at 960 review each SNR and identifythe lowest SNR. The beat segment having the lowest SNR is labeled as thesuspect beat segment.

Additionally, or alternatively, at 960, the one or more processors mayapply an additional or separate analysis to identify a sensitivity limitof each of the successive beat segments of the modified CA signal. Thenumber of beats within one beat segment is defined by a search windowthat is overlaid onto the CA signals. The length of the search window(and beat segment) may vary, provided that the search window encompass adesired number of beats sufficient to enable the process to identify asensitivity limit for each beat segment. The sensitivity limit for anygiven beat segment may be defined in various manners. For example, thesensitivity limit may be defined based on a dynamic range of themodified CA signals during a corresponding beat segment.

At 962, the one or more processors identify a sinus segment that islocated proximate to the suspect beat segment. For example, the sinussegment may correspond to a beat segment that immediately precedes thesuspect beat segment or precedes the suspect beat segment by apredetermined number of beats or amount of time. A beat segment may becharacterized as “a sinus segment” in various manners, such as when thebeat segment exhibits greater amplitude signals than the suspect beatsegment, amplitudes that are greater than the suspect beat segment by apredetermined amount or amplitudes that exceed a programmed level.Additionally, or alternatively, the sinus segment may be defined basedon other criteria. For example, the sinus segment may be defined tocorrespond to a beat segment preceding or following the suspect beatsegment, where the candidate sinus segment has an SNR, integral/energyand/or derivative (as determined at 954C, 954B and 954A) that satisfiesthe corresponding threshold.

At 964, the one or more processors identify the integral and/orderivative of the CA signals within the suspect beat segment. The one ormore processors also identify the integral and/or derivative of the CAsignals within the sinus segment. At 966, the integral and/or derivativeof the suspect beat segment is compared to the integral and/orderivative of the sinus segment. For example, the comparison at 966 maydetermine whether the segments of the suspect and sinus signals aresimilar or within a predetermined range of one another. Additionally, oralternatively, the comparison at 966 may determine whether thederivatives of the suspect and sinus signals are similar or within apredetermined range of one another. When the integrals and/orderivatives are sufficiently similar, flow moves to 968. Alternatively,with the integrals and/or derivatives differ by a predetermined amount,flow moves to 970.

At 966, the one or more processors determine whether the suspect beatsegment results from an under sensed segment or whether the suspect beatsegment indicates an arrhythmia (e.g., a presence of bradycardia orasystole). The processors determine whether under sensing occurred basedon a comparison of the integrals and/or derivatives of the suspect andsinus segments. The processors may determine at 966 that a bradycardiaor asystole episode does not exist where the integrals and/orderivatives of the suspect and sinus segments are within predeterminedranges of one another.

At 968, the one or more processors declare the suspect beat segment torepresent an under sensed segment and to remove the suspect beat segmentfrom the modified CA signals that may be further analyzed. By removingthe suspect beat segment, as an under sensed segment, the process ofFIG. 9C avoids false detection of unstable beats (e.g., bradycardia andasystole episodes).

At 970, the one or more processors declare the suspect beat segment tobe an arrhythmia (e.g., asystole or bradycardia segment). The suspectbeat segment may be declared a bradycardia segment based on bradycardiacriteria, while the suspect beat segment may be declared an asystolesegment when the segment satisfies asystole criteria. For example, thebradycardia criteria may be that X bradycardia beats occurred out of Ytotal beats. As another example, the asystole criteria may be that apredetermined period of time has passed without any device detectedactivity in the CA signal.

Optionally, the processors may perform the bradycardia and/or asystoleanalysis at 970 to perform the declaration. Alternatively, the suspectbeat segment may already have been declared a potential bradycardiasegment or asystole segment at an earlier point in the process. When thesuspect beat segment is already declared a potential bradycardia orasystole segment, at 970, the processors merely confirm the priordetermination.

FIG. 10D illustrates an example of a CA signal that is analyzed inaccordance with the process of FIG. 9C. In FIG. 10D, the CA signal 1060includes a series of cardiac events or beats 1062, from which the ICMhas identified device detected R-waves denoted by R-wave marker 1064(e.g., a dot) superimposed upon the cardiac events. The ICM has alsodeclared one or more beats 1065 to be non-sinus (e.g., asystole). Inaccordance with the operations of FIG. 9C, a beat segment that has thelowest sensitivity level within the modified CA signal is identified andlabels as a suspect beat segment 1066. The sinus segment 1068 isidentified as located proximate to the suspect beat segment 1066. Forexample, the sinus segment 1068 may correspond to a beat segment thatprecedes the suspect beat segment 1066 by a predetermined number ofbeats or amount of time. The process of FIG. 9C identifies whether theintegral and/or derivative of the suspect beat segment 1066 is similarto the integral and/or derivative of the sinus segment 1068. The falseasystole detection at 1065 is “ruled out” or rejected when the integraland/or derivative of the suspect and sinus segments 1066 and 1068 arewithin predetermined ranges of one another.

Returning to FIG. 9C, following 968 and 970, at 972, the one or moreprocessors determine whether additional suspect beat segments weredeclared in the CA signals. If so, flow moves to 974, where the processshifts to the next suspect beat segment. Otherwise, the process ends at976.

FIG. 10E illustrates an example of CA signals analyzed by a process, inaccordance with an embodiment herein, for setting upper and lower boundson sensitivity levels. In FIG. 10E, a CA signal 1070 is illustrated withmarkers 1071 detected by an ICM in accordance with a conventional ORIprocess. As noted by beat segment 1072, the conventional ORI processfails to detect certain R-waves due to under-sensing. FIG. 10E alsoillustrates a modified CA signal 1074 that is formed by processing theoriginal CA signal 1070 in accordance with embodiments herein that applyfeature enhancement to form enhanced R-wave features. The modified CAsignal 1074 is also analyzed to identify beat segments of interest andto calculate upper and lower sensitivity bounds based on the enhancedR-wave features. The upper and lower bounds on sensitivity levels arecalculated based on the enhanced R-wave features from and immediatelypreceding R-wave, not simply based on prior stable beats. An upperboundary (minimum sensitivity) is defined at 1075. The process hereinadjusts the upper bound of sensitivity level based on a group of priorsuccessive R-wave peak amplitudes. For example, peak amplitudes ofR-waves 1076 are used to define the upper sensitivity level duringsuccessive beats as noted at 1075. By applying the feature enhancementto enhanced R-wave features and the adaptive lower bound on sensitivitylevel, the embodiment associate with FIG. 10E detects R-waves (as notedat markers 1078 during the suspect beat segment 1072).

In accordance with the processes of FIGS. 9B and 9C, a the adaptivesensitivity level is bound by a lower bound 1077 that varies withnon-ventricular signal 1078 (outside both R-wave 1076 and T-wave 1079intervals). For example, the lower sensitivity bound is calculated asmean +2 standard deviation of CA signal in the preceding non-ventricularinterval. Utilizing upper and lower sensitivity bounds protects againstover-sensing on noise or large P-waves, while allowing sensitivity to golower during periods of small R-waves.

As explained herein, a conventional R-wave sensing algorithm adjusts atime-varying sensing level based on a sensitivity profile illustrated inFIG. 2B, for which the control parameters are fixed (e.g., refractoryperiod duration, threshold start percentage, decay delay duration, lowerbound or maximum sensitivity, etc.). Turning to FIG. 2B, the startsensitivity threshold parameter 161 defines a start level of thesensitivity profile 153. For example, the start sensitivity parametermay set a start sensitivity to a percentage of the preceding R-wave peakamplitude. The refractory period duration parameter 159 defines ablanking interval beginning at a sensed R-wave 155, during which theprocessors do not search for CA signal. When the profile includes alinear sensitivity level decline 153, the decay delay rate defines aslope of the linear sensitivity level decline. The maximum sensitivityparameter 157 defines a lowest sensitivity level (e.g., maximumsensitivity) that linear sensitivity decline is allowed to reach. Thedecay delay parameter 169 defines the interval at which the sensitivityprofile maintains the sensitivity level at a constant level followingexpiration of the refractory period before the sensitivity profilebegins decreasing.

Although a physician can reprogram the values, in the conventionalR-wave sensing algorithm, there is no self-learning based on real timeCA signal features. Also, the conventional R-wave sensing algorithmdiscussed herein does not consider T-wave amplitude information. Theforegoing limitations of the conventional R-wave sensing algorithm maylead to unintended under-sensing or over-sensing under abrupt change insignal morphology, amplitude, and heart rate.

The process of FIG. 9D adaptively adjusts the sensitivity profile beatby beat (or ensemble by ensemble) based on R-wave and T-wavecharacteristics of interest. The process of FIG. 9D follows multipleparallel determinations along the parallel paths at 929, to dynamicallyadjust parameters of the sensitivity profile (e.g., start threshold,refractory period, decay delay, decay delay rate and max sensitivity)

At 931, the one or more processors obtain a CA signal for a beat and/orensemble of beats. At 932, the one or more processors identify a riserate of the current beat/ensemble and determine whether the currentR-wave rise rate exceeds an R-wave rise rate of a precedingbeat/ensemble by more than a R-wave rise rate threshold. If so, thecurrent R-wave has a “rapid rise rate characteristic”. The R-wave riserate threshold may be calculated based on an R-wave rise rate of anindividual prior beat and/or an ensemble of prior beats. Alternatively,the R-wave rise rate threshold may be predetermined (e.g., clinicianprogrammed). A newly sensed R-wave will have a much larger R-wave riserate compared to prior beats when a PVC occurs. When the differencebetween the current and prior R-wave rise rates exceed a certainthreshold, flow moves to 940. Otherwise, the process does not update anyparameters of the sensitivity profile based on the decision at 932.Regardless of the determination at 932, flow continues along theparallel branch 929. At 940, the one or more processors modify a startsensitivity parameter that defines a start sensitivity of thesensitivity profile. For example, the start sensitivity parameter mayset start sensitivity to a percentage of the preceding R-wave peakamplitude (e.g., 75% of the peak of the preceding R-wave). At 940, theprocessors reduce the start sensitivity parameter to a lower percentagevalue of the R-wave peak amplitude (e.g., reduce from 75% to 50%).Alternatively, flow branches to 941, where the processors may retain thestart sensitivity percentage at the same level, but instead theprocessors set the start sensitivity as the predetermined percentage ofthe average peak amplitude for an ensemble of prior beats.

Next, the discussion concerns the operations at 933-936, where the oneor more processors analyze R-wave and T-wave characteristics of interest(COI) in connection with modifying a refractory period and/or decaydelay parameter of the sensitivity profile. At 933, the one or moreprocessors determine whether the current beat/ensemble exhibits “a rapidrise rate characteristic” in the R-wave that exceeds an R-wave rise ratethreshold in the same manner as discussed above at 932. When a rapidrise characteristic is identified, flow continues to 943. Otherwise, theprocess does not update any parameters of the sensitivity profile basedon the determination at 933.

At 934, the one or more processors determine whether the currentbeat/ensemble exhibits a T-wave peak having a proximity to the R-wavesensing refractory period, that exceeds a T-wave refractory threshold.If so, flow moves to 943. Otherwise, no sensitivity profile parametersare updated based on T-wave refractory proximity. If the separationbetween the T-wave peak location and the end of R-wave sensingrefractory period in prior beats is greater than the T-wave—refractorythreshold (e.g., 100 msec), at 943, the processors increase therefractory period by a predetermined amount (e.g., 50 msec) in order toavoid T-wave over-sensing in future beats.

At 935, the one or more processors determine whether the currentbeat/ensemble exhibit a T-wave peak amplitude, relative to the R-waveamplitude, that exceeds a T/R-wave amplitude ratio threshold. If so,flow moves to 943. Otherwise, no sensitivity profile parameters areupdated based on the T/R-wave amplitude ratio. The processors may assessthe T-wave amplitude in previous beats by searching for the peakamplitude in a T-wave search window that is temporally positioned afterthe sensed R-wave. FIG. 10A illustrates an example for identifyingT-wave peak amplitude. FIG. 10A illustrates a CA signal, for which anR-wave is detected. Following detection of the R-wave, a refractoryperiod is set (e.g., 238 ms). Following the refractory period, a T-wavesearch window is defined having a predetermined duration. For example,the duration of the T-wave search window may represent the differencebetween the Q-T interval (QTI) upper limit (e.g., 410 ms) and therefractory. The processors analyze the CA signal within the T-wavesearch window to identify a peak of the T-wave. Alternatively, theT-wave search window could be heart rate dependent. With reference toFIG. 10A, first an upper limit of the Q-T interval is calculated. TheT-wave search window will start from [QTI_limit—refractory period] to[QTI_limit], with both values set relative to the timing of a sensedR-wave. Once the peak T-wave is found, the processors register theT-wave peak amplitude and time relative to the sensed R-wave.

Returning to FIG. 9D, at 935, if the T/R wave amplitude ratio exceeds athreshold (e.g., T-wave amplitude/R-wave amplitude>=0.8) in prior beats,at 943, the processors increase the refractory period parameter of thesensitivity profile (e.g., T-wave peak time+20 msec) in order to avoidT-wave over-sensing.

Additionally, or alternatively, at 943, the processors may increase thedecay delay parameter.

At 936, the one or more processors determine whether the currentbeat/ensemble exhibits a “fast heart rate characteristic”, whereby theheart rate exceeds a heart rate threshold (e.g., 100 bpm). When theheart rate exceeds the heart rate threshold, flow moves to 942. At 942,the one or more processors scale the refractory period parameter and/orscale the decay delay parameter relative to the heart rate. For example,with a fast heart rate, the processors may decrease the refractoryperiod parameter based on a scaling factor to avoid under-sensing. Forexample, the processors may set the refractory period=250 msec*(baserate/current heart rate), where the base rate=60 bpm. If it is assumedthat the current heart rate is 100 bpm, then the new refractory periodwill be 150 msec. The processors may scale the decay delay in a similarmanner based on a desired decay delay scaling factor. Alternatively,when the heart rate is not declared to be rapid, no sensitivity profileparameters are updated based on a fast heart rate characteristic.

At 937, the one or more processors determine whether the currentbeat/ensemble exhibit a T-wave peak amplitude, relative to the R-waveamplitude, that exceeds a T/R-wave amplitude ratio threshold. If so flowmoves to 944. The processors perform a T-wave amplitude search similarto the process described above for adjusting refractory period. When theT/R wave amplitude ratio (T-wave amplitude divided by R-wave amplitude)is greater than 0.5, the processors decreases the decay delay rateparameter (e.g., use a lower mV/msec decay delay speed) in order toavoid T-wave over-sensing. For example, the processors may decrease thedecay delay rate parameter by 0.5 uV/msec at every 5% increase in theT/R wave amplitude ratio.

At 938, the one or more processors determine whether the R-wave riserate exceeds an R-wave rise rate threshold. If so, flow moves to 945where the processors increase the decay delay rate parameter for thesensitivity profile. When the newly sensed R-wave (such as a PVC beat)has much larger amplitude R-wave compared to prior beats, the processorsuse higher decay delay speed to avoid under-sensing of the next beat.

At 939, the one or more processors estimate a lower bound for thetime-varying sensitivity profile. For example, the processors may setthe lower bound for the time-varying sensitivity profile to be scaledsum of a mean and standard deviation of non-ventricular portions of theCA signals for a desired number of beats. The non-ventricular portionscorrespond to portions of the beats that are outside of the R-wave andthe T-wave intervals. For example, the processors may overlay a searchwindow (e.g., 2 seconds) over non-ventricular activity portions of theCA signals prior to a current beat. The processors calculate amean/standard deviation values for the non-ventricular activity portionsof the CA signals outside the R-wave and T-waves to estimate the lowerbound of the time-varying sensitivity profile (e.g., as mean+2*standarddeviation). During beats of decreased background noise or small P-waves,the lower bound of the sensitivity profile would decrease, while duringbeats of increased background noise or large P-waves, the lower bound ofthe sensitivity profile would increase.

At 946, the one or more processors adjust the lower bound ofsensitivity, such as adjusting the maximum sensitivity to be utilized inthe sensitivity profile.

At 947, the one or more processors update the sensitivity profileparameters that were adjusted at 940-946. The process of FIG. 9D adjustsone or more of the sensitivity profile parameters on a beat-by-beatbasis. Additionally, or alternatively, adjustment may be done on aregular time (e.g., every one minute) or on-demand when specificconditions are met. The process of FIG. 9D will track the beat-by-beatchanges in heart rate, R-wave amplitude, and T-wave amplitude, andadjust sensitivity profile parameters only when abrupt changes ofinterest occur in the CA signal. It is recognized that somewhat similaroperations may be performed at different points in the process of FIG.9D. For example, at 933 and 938, the one or more processors analyzeR-wave amplitudes for a rapid rising characteristic. As another example,at 935 and 97, the one or more processors compare a ratio between T-waveand R-wave amplitudes. When similar operations are performed atdifferent points in the process of FIG. 9D, the same or differentthresholds may be utilized. For example, a common or different rapidrise thresholds may be utilized at 933 and 938, and a common ordifferent T/R wave ratio thresholds may be utilized at 935, 937. Inaddition, it is recognized that the adjustments at 940-946 may adjust acommon parameter in complementary or counteracting/subtractive manners.For example, at 943 and 945, the process may increase the decay delayrate parameter, while the operation at 944 may decrease the decay delayrate parameter. The amount of increase or decrease in each parameter ateach of 940-946 may vary.

The process of FIGS. 9A-9D was tested on a data set of 996 episodescollected from field devices and found to perform superior to aconventional ORI process. The process of FIGS. 9B and 9C exhibitedimprovement of positive predictive value to 88%, improvement of relativesensitivity 93% and reduction of false detection rate of 84% inbradycardia and asystole, respectively, as compared to the conventionalORI process.

The process of FIGS. 9B and 9C is computationally inexpensive as it usessimple operations (such as comparisons, additions, multiplications andmoving averages) that can be implemented efficiently inpower-constrained platform and thus may be implemented on multipleplatforms (e.g., ICM device, local external device or cloud server) andperformance on these multiple platforms could be mirrored with very highdegree of agreement.

The processes for FIGS. 9A, 9D afford the ability to dynamically adjustarrhythmia detection parameters and thresholds based on previous historypreceding a suspected detection such as by adjusting refractory period,starting threshold, maximum sensitivity lower bound and T-waveamplitude. The processes for FIGS. 9A-9C provide unique and simple waysto detect un-physiologic segments of data to avoid false detections onlow quality data, and may be applied on a secondary channel, a primarychannel or combination thereof.

Additionally, or alternatively, the processes of FIGS. 3, 4, 9A-9D maybe implemented partly in firmware of the ICM, while the remainingoperations are implemented off-line on an external device or remoteserver. As a further example, the operations of FIGS. 3, 4, 9A-9D may bedivided in other manners between the ICM, an external device and/orremote server.

As another example implementation, the process of FIGS. 3, 4, 9A-9D maybe implemented in a cloud-based environment, such as on a remote serverand/or distributed between multiple remote processors/servers. Forexample, a remote server at a medical network may receive CA data setsgenerated by an ICM. The remote server may analyze the CA data setsoff-line, such as in an effort to identify false positive AF detectionsthat are declared by the ICM. The remote server may collect multiple CAdata sets and perform batch type processing to analyze multiple CA datasets at one point in time. A remote server or cloud-based networkprovides substantial computational power well in excess of what would beneeded to efficiently process even a large batch of episodeelectrograms. Hence, a remote server or cloud-based network would affordbetter R-wave detection and improved arrhythmia discrimination for AFepisodes before the CA data sets are presented to a clinician, therebyremoving or substantially reducing the amount of time required by aclinician to parse through false positives and distinguish truearrhythmias from false arrhythmias.

Additionally, or alternatively, the processes of FIGS. 3, 4, 9A-9D maybe implemented on a local external device, such as a smart phone, tabletdevice or other device in the possession of the patient. Mobile devicessuch as smart phones are configured with a ICM application configured tocommunicate with the ICM. The mobile devices are capable of processingCA data sets as described herein. The mobile device may make adetermination, based on the enhanced R-wave detection processesdescribed herein, whether an episode represents a true arrhythmia orinstead a false positive driven by inappropriate R-waves. The mobiledevice may screen out the false positive episodes and prevent the falsepositive episodes from being uploaded to the remote server.Additionally, or alternatively, the mobile device may also providefeedback communication to the ICM (e.g., informing the ICM of the falsepositives) to allow the ICM to update diagnostic counts that aremaintained therein.

In accordance with at least some embodiments, one or more processorsherein may identify that the set of device declared R-waves (asdesignated by the ORI process in the ICM) significantly differs from theconfirmation R-waves identified in connection with the process of FIGS.3, 4, 9A-9D. When substantial differences are identified, the one ormore processors may send an alert to a clinician to check and/orreprogram sensitivity parameters. Optionally, a “simulated ORI” processmay be implemented to analyze a CA data set offline (e.g., in the cloud)upon detection of such different sets of R-waves. The simulated processmay test one or more potential ORI configurations and identifies animproved set of device-programmable ORI parameters to decrease the“false-positive burden” at the ICM. Optionally, the improved ORIprogramming parameters may be automatically pushed back to remotelyprogram the ICM. Applied more broadly to a sufficiently long CA data set(up to and including the entirety of 24-hour monitoring), the R-wavedetection processes herein can provide detailed ventricular rhythmdiagnostics.

V. ALTERNATIVE EMBODIMENT—FULLY ADAPTIVE R-WAVE DETECTION/CORRECTIONALGORITHM (1^(ST) & 2^(ND) PASS)

In accordance with embodiments herein, a computer implemented method isdescribed for detecting arrhythmias in cardiac activity. The methodcomprises, under control of one or more processors configured withspecific executable instructions, obtaining a far field cardiac activity(CA) data set that includes far field CA signals for a series of beats;i) applying an initial R-wave detection process to the CA signals anddesignating R-wave markers in the CA data set, the R-wave markersseparated by RR intervals; ii) applying an R-wave confirmation processthat comprises: ii)(a) calculating instantaneous and average RRintervals between the R-wave markers designated by the initial R-wavedetection process; ii)(b) identifying a suspect beat segment from the CAsignals based on a relation between the instantaneous and average RRintervals; ii)(c) searching the suspect beat segment for a potentialunder detected beat by comparing the suspect beat segment to one or moreQRS templates; and ii)(d) when an under detected beat is identified fromthe suspect beat segment, designating a new R-wave marker within the CAdata set corresponding to the under detected beat; iii) detecting anarrhythmia within CA data set based on the R-wave markers designatedduring the initial R-wave detection process and R-wave confirmationprocess; and iv) recording results of the detecting of the arrhythmia.

Additionally, or alternatively, the method may further compriseanalyzing the relation between the instantaneous and average RRintervals, wherein the instantaneous and average RR intervals areidentified by stepping through successive beat segments along the CAsignal to search for potential under detected beats, the instantaneousRR interval representing an interval between a current R-wave marker andone of a preceding and succeeding R-wave marker, the average RR intervalrepresenting an average interval for a predetermined number of RRintervals related to a current RR interval. Additionally, oralternatively, the relation utilized to identify the suspect beatsegment represents a difference between the instantaneous and average RRintervals that exceeds an RR interval range threshold. Additionally, oralternatively, the identifying the suspect beat segment furthercomprising overlaying a search window on a current beat segment andcomparing i) the instantaneous RR interval for a current beat within thesearch window and ii) the average RR interval corresponding to acollection of beats within the search window, the comparison beingperformed while iteratively stepping the search window through the CAsignals beat by beat. Additionally, or alternatively, the method,further comprises building a library of QRS templates based onmorphologies of beats detected in the CA signals; identifying a currentQRS segment from the CA signals; comparing the current QRS segment tothe library of QRS templates; when the current QRS segment does notmatch the QRS templates, adding a new QRS template to the library basedon the current QRS segment. Additionally, or alternatively, the methodfurther comprises identifying a time of peak amplitude in the suspectbeat segment and designating a candidate R-wave marker at the time ofpeak amplitude; calculating a candidate RR interval between thecandidate R-wave marker and a previous R-wave marker; comparing thecandidate RR interval to an RR interval threshold indicative of anactual RR interval; and when the new RR interval falls below the RRinterval threshold, declaring the candidate R-wave marker to be falseand that no under detection occurred. Additionally, or alternatively,the method further comprising searching for an R-wave within the CAsignal based on a signal envelope and local maxima; when the R-wave isdetected, set a refractory period during which no additional R-waves aresearched; and following termination of the refractory, searching for anext R-wave within a corresponding next signal envelope. Additionally,or alternatively, the method further comprising adjusting a duration ofthe refractory interval based on the RR interval and T-wave peaklocation of the previous beats. Additionally, or alternatively, themethod further comprising applying a feature enhancement to the CAsignals to form enhanced R-wave or T-wave features in the CA data set.

In accordance with alternative embodiments, a system is provided fordetecting arrhythmias in cardiac activity. The system comprises: memoryto store specific executable instructions; and one or more processorsconfigured to execute the specific executable instructions for:obtaining a far field cardiac activity (CA) data set that includes farfield CA signals for a series of beats; applying an initial R-wavedetection process to the CA signals and designating R-wave markers inthe CA data set, the R-wave markers separated by RR intervals; applyingan R-wave confirmation process. The R-wave confirmation processcomprises: calculating instantaneous and average RR intervals betweenthe R-wave markers designated by the initial R-wave detection process;identifying a suspect beat segment from the CA signals based on arelation between the instantaneous and average RR intervals; searchingthe suspect beat segment for a potential under detected beat bycomparing the suspect beat segment to one or more QRS templates; andwhen an under detected beat is identified from the suspect beat segment,designating a new R-wave marker within the CA data set corresponding tothe under detected beat. The one or more processors are furtherconfigured for detecting an arrhythmia within CA data set based on theR-wave markers designated during the initial R-wave detection processand R-wave confirmation process; and recording results of the detectingof the arrhythmia.

Additionally, or alternatively, the processor is further configured toanalyze the relation between the instantaneous and average RR intervals,wherein the instantaneous and average RR intervals are identified bystepping through successive beat segments along the CA signal to searchfor potential under detected beats, the instantaneous RR intervalrepresenting an interval between a current R-wave marker and one of apreceding and succeeding R-wave marker, the average RR intervalrepresenting an average interval for a predetermined number of RRintervals related to a current RR interval. Additionally, oralternatively, the relation utilized to identify the suspect beatsegment represents a difference between the instantaneous and average RRintervals that exceeds an RR interval range threshold. Additionally, oralternatively, the processor is further configured to apply a featureenhancement to the CA signals to form enhanced R-wave or T-wave featuresin the CA data set. Additionally, or alternatively, the processor isfurther configured to identify the suspect beat segment by overlaying asearch window on a current beat segment and comparing i) theinstantaneous RR interval for a current beat within the search windowand ii) the average RR interval corresponding to a collection of beatswithin the search window, the comparison being performed whileiteratively stepping the search window through the CA signals beat bybeat. Additionally, or alternatively, the processor is furtherconfigured to build a library of QRS templates based on morphologies ofbeats detected in the CA signals; identify a current QRS segment fromthe CA signals; compare the current QRS segment to the library of QRStemplates; when the current QRS segment does not match the QRStemplates, add a new QRS template to the library based on the currentQRS segment. Additionally, or alternatively, the processor is furtherconfigured to identify a time of peak amplitude in the suspect beatsegment and designating a candidate R-wave marker at the time of peakamplitude; calculate a candidate RR interval between the candidateR-wave marker and a previous R-wave marker; compare the candidate RRinterval to an RR interval threshold indicative of an actual RRinterval; and when the new RR interval falls below the RR intervalthreshold, declare the candidate R-wave marker to be false and that nounder detection occurred.

Additionally, or alternatively, the processor is further configured tosearch for an R-wave within the CA signal based on a signal envelope andlocal maxima; when the R-wave is detected, set a refractory periodduring which no additional R-waves are searched; and followingtermination of the refractory, search for a next R-wave within acorresponding next signal envelope. Additionally, or alternatively, theprocessor is further configured to adjust a duration of the refractoryinterval based on the RR interval and T-wave peak location of theprevious beats. Next, a detailed description is provided for one or moremethods and systems to implement the foregoing embodiments.

FIG. 9E illustrates a process for R-wave detection in accordance withembodiments herein. Prior to the R-wave detection process of FIG. 9E,the one or more processors may bandpass filter and rectify the CAsignals as explained elsewhere herein.

At 980, the one or more processors utilize a moving window to calculatean R-wave envelope for the CA signals. For example, the processors mayapply a moving average filter (e.g., 90 second average). The movingaverage filter is used to calculate the signal envelope, from which asignal floor is determined. For example, the signal floor of the signalenvelope may be defined as a percentile of the signal envelope profile(e.g., 2.5 percentile). Additionally, or alternatively, when the signalfloor is determined to be too low (e.g., below 0.05 mV), the processorsmay redefine the signal floor as a different percentile of the signalenvelope (e.g., 25 percentile). For example, it may be desirable toadjust the signal floor, such as when the CA signal includes segmentsthat have little or no activity for longer than the moving averagewindow size.

FIG. 10F illustrates a portion of a CA signal processed in accordancewith the operation at 980 in FIG. 9E. FIG. 10F illustrates a filtered,rectified CA signal 1081, to which a feature enhancement function hasbeen applied. A signal envelope 1083 is calculated by the moving averagefilter. Once the signal envelope 1083 is determined, the processors thendefine a signal floor 1085. The signal floor 1085 is defined tocorrespond to a predetermined percentile of the signal envelope 1083.

Returning to FIG. 9E at 982, the one or more processors search forR-waves within the CA signal. The processors apply a sensitivitythreshold (e.g., 0.8 times the signal floor) to search local maximagreater than the sensitivity limit in the rectified CA signal andidentify them as the R-waves. With reference to FIG. 10E, a local maxima1087 is designated as the peak of an R-wave, the processors set arefractory period (e.g., 250 ms), during which the processors do notsearch for additional R-waves. When the refractory period terminates,the processors identify the next local maxima therein.

At 984, the one or more processors determine whether the refractoryperiod should be adjusted, such as in connection with a fast heart rate.In connection therewith, the processors calculate R-R intervals (RRIs)for the R-waves detected at 982 and determine a statisticalcharacteristic of interest related to the RRIs, such as the median. Ifthe median RRI (or another statistical characteristic of interest) isoutside of a predetermined limit (e.g., the median is less than 500msec), the one or more processors adjust the refractory period (e.g.,shorten it from 250 to 150 ms). The processors repeat the operation at982 (as denoted by dashed line 983) in order to find new local maximaindicated R-waves based on a shorter refractory period (e.g., 150 msec).At 984, the processors obtain peak R-wave amplitudes for all or aportion of the beats that were detected. At 984, optionally, the one ormore processors remove any R-waves that were detected within an initialintroductory segment or within a final closing segment of the completeCA signal (e.g., the first second and the last second of the CA signal).The initial and final segments of the CA signals are removed in order toavoid boundary conditions that may otherwise affect subsequentoperations described herein. The operations at 982-984 are repeateduntil the RRI have a statistical characteristic of interest thatsatisfies a predetermined limit. Thereafter, flow advances to 986.

At 986, the one or more processors build a library of QRS templatesbased on the beats detected at 982-984. In connection therewith, theprocessors apply a window (e.g., 100 msec window) to each beat of the CAsignals wherein the window is set relative to a corresponding devicedocumented R-wave marker. For example, a 100 ms window may be centeredat the peak of the R-wave (corresponding to the device documented R-wavemarker), where the processors capture pre- and post-R-wave CA signals toextract an individual QRS segment. The individual QRS segment iscompared to QRS morphology templates already saved in the library. Whenthe current QRS segment matches (or is substantially similar to) anexisting QRS morphology template, the library does not need to beupdated. Alternatively, when the current QRS segment differs from theQRS morphology templates in the library, a new QRS morphology templateis formed based on the current QRS segment and is added to the library.The processors may utilize various criteria to determine whether acurrent QRS segment matches or differs from existing QRS morphologytemplates. For example, the processors may apply cross-correlationbetween the current QRS segment and one or more QRS morphologytemplates, where a result of the cross-correlation is compared with amatch threshold (e.g., cross-correlation coefficient greater than 0.5,and/or cross-correlation peak is less than 20 msec.).

At 988, the one or more processors calculate instantaneous and averageRR intervals based on sets of RR intervals for the CA signal. Forexample, the processors may calculate the instantaneous RR intervals asan interval between a current R-wave marker and a preceding R-wavemarker and/or a current R-wave marker and succeeding R-wave marker. Asanother example, the processors may calculate an average RR interval byaveraging a predetermined number of RR intervals related to a current RRinterval (e.g., four of the most recent RRI values preceding a currentR-wave marker).

At 990, the one or more processors step through successive beat segmentsalong the CA signal to search for potential under detected beats. Theprocessors identify potential under detection based on “abrupt” changesbetween instantaneous and moving averages for the RRI. For example, therelation utilized to identify the suspect beat segment may represent adifference between the instantaneous and average RR intervals thatexceeds an RR interval range threshold. To search for differencesoutside the RR interval range threshold, the processors overlay a searchwindow on a current beat segment (e.g., a single beat or a series of twoor more beats). The processors compare i) the instantaneous RRI for thecurrent beat within the search window and ii) the average RRIcorresponding to a collection of beats within the search window. Theprocessors perform the comparison while iteratively stepping the searchwindow through the CA signals beat by beat. The processors designatepotential under detected beats when the comparison exceeds certainlimits. For example, the processors designate a potential under detectedbeats when the instantaneous RRI exceeds an initial limit and theinstantaneous and average RRIs vary from one another by more than apredetermined amount. For example, when the instantaneous RRI is greaterthan 500 msec and the instantaneous RRI is greater than the average RRIby more than 100 msec, the processors may determine that a potentialunder detected beat has occurred within the suspect beat segment.

At 990, when a potential under detected beat is identified, theprocessors define a search window for the potential under detected beat.The search window is positioned between two successive detected R-waves.For example, the search window may begin a predetermined period of timeafter a previous detected R-wave and extend until a predetermined periodof time before a next successive detected R-wave. For example, theprocessors may extract a suspect beat segment from the CA signal, wherethe suspect beat segment is searched for the potential under detectedbeat/R-wave. The suspect beat segment may begin 150 msec after theprevious detected R-wave marker and continue up to 100 msec before thecurrent R-wave marker. The processors compare the suspect beat segmentto one or more QRS morphology templates within the library. For example,the comparison may be based on correlation (e.g., >0.5 correlationcoefficient). If the suspect beat segment includes a shape that does notmatch, or is sufficiently different from, the QRS morphology templates,the processors determined that no under detection occurred and theoperation at 992 is skipped. Alternatively, if the suspect beat segmentmatches or is sufficiently similar to a QRS morphology template, theprocessors determined that under detection did occur and flow moves to992.

At 992, the one or more processors identify a time of peak amplitude inthe suspect beat segment and temporarily designate a candidate R-wavemarker at the time of peak amplitude as a candidate R-wave. Theprocessors calculate a candidate RRI between the candidate R-wave markerand a previous R-wave marker (as detected at 982). The processorscompare the new RR interval to an RR interval threshold that is definedas a shortest or lowest RR interval that is acceptable as an actual orlegitimate RR interval. If the new RR interval falls below the RRInterval threshold, the new RR interval is considered too short torepresent an actual or legitimate RR interval and thus, the processorsdeclare the candidate R-wave and marker to be false and determine thatno under detection occurred.

Alternatively, if the new RRI is greater than a predetermined limit(e.g., 200 msec,), the processors further analyze the new beat. At 992,the one or more processors calculate the peak amplitude new/actualR-wave within the beat segment. If the peak amplitude is greater than apredetermined limit (e.g., 0.5 times the minimum value of detectedR-wave amplitudes), the processors confirm under-detection. The new beatand new R-wave marker are designated/registered within the CA data setand the process ends.

Optionally, after identifying R-waves within the CA signal, P-wavesearch windows and/or T-wave search windows may be defined in connectionwith some or all of the beats. For example, a P-wave search window maybe defined to extend 300 ms before the R-wave, while a T-wave searchwindow may be defined to extend 200 ms after the R-wave. Accordingly, byimproving the accuracy of R-wave detection in the foregoing manner,embodiments herein also improve the accuracy of identifying P-waves andT-waves, as well as other features of interest within cardiac events.

The process of FIG. 9E may be implemented in connection with varioustypes of systems. For example, the process of FIG. 9E may be implementedas a “re-detection” process in firmware of a device, such as an ICM.While the process of FIG. 9E may be more computationally expensive, ascompared to a traditional ORI process, the process of FIG. 9E may beconfigured to run on an ICM as part of a confirmation or re-detectionscheme. For example, CA signals initially analyzed by the ICM (utilizinga conventional ORI process) may be stored in a spin buffer when theconventional ORI process identifies an arrhythmia episode. When the CAsignals are stored in the spin buffer, the process of FIG. 9E may beapplied to the CA signals as a confirmation or re-detection scheme.

Additionally, or alternatively, CA signals may be processed with variousfront end filters and along different sensing channels by an ICM. Thesignal filtered and processed along one sensing channel may betemporarily buffered, while the conventional ORI process operates uponCA signals being received over a separate sensing channel. When the ORIprocess identifies potential arrhythmia, the ICM may apply the processof FIG. 9E to the buffered CA signals.

Additionally, or alternatively, the process of FIG. 9E may beimplemented partly in firmware of the ICM, while the remainingoperations of FIG. 9E are implemented off-line on an external device orremote server. For example, the operations at 980-984 may be implementedin firmware of the ICM, while the remaining operations at 986-992 may beimplemented off-line on an external device or remote server. As afurther example, the operations of FIG. 9E may be divided in othermanners between the ICM, an external device and/or remote server.

As another example implementation, the process of FIG. 9E may beimplemented in a cloud-based environment, such as on a remote serverand/or distributed between multiple remote processors/servers. Forexample, a remote server at a medical network may receive CA data setsgenerated by an ICM. The remote server may analyze the CA data setsoff-line, such as in an effort to identify false positive AF detectionsthat are declared by the ICM. The remote server may collect multiple CAdata sets and perform batch type processing to analyze multiple CA datasets at one point in time. A remote server or cloud-based networkprovides substantial computational power well in excess of what would beneeded to efficiently process even a large batch of episodeelectrograms. Hence, a remote server or cloud-based network would affordbetter R-wave detection and improved arrhythmia discrimination for AFepisodes before the CA data sets are presented to a clinician, therebyremoving or substantially reducing the amount of time required by aclinician to parse through false positives and distinguish truearrhythmias from false arrhythmias.

Additionally, or alternatively, the process of FIG. 9E may beimplemented on a local external device, such as a smart phone, tabletdevice or other device in the possession of the patient. Mobile devicessuch as smart phones are configured with a ICM application configured tocommunicate with the ICM. The mobile devices are capable of processingCA data sets as described herein. The mobile device may make adetermination, based on the enhanced R-wave detection processesdescribed herein, whether an episode represents a true arrhythmia orinstead a false positive driven by inappropriate R-waves. The mobiledevice may screen out the false positive episodes and prevent the falsepositive episodes from being uploaded to the remote server.Additionally, or alternatively, the mobile device may also providefeedback communication to the ICM (e.g., informing the ICM of the falsepositives) to allow the ICM to update diagnostic counts that aremaintained therein.

In accordance with at least some embodiments, one or more processorsherein may identify that the set of device declared R-waves (asdesignated by the ORI process in the ICM) significantly differs from theconfirmation R-waves identified in connection with the process of FIG.9E. When substantial differences are identified, the one or moreprocessors may send an alert to a clinician to check and/or reprogramsensitivity parameters. Optionally, a “simulated ORI” process may beimplemented to analyze a CA data set offline (e.g., in the cloud) upondetection of such different sets of R-waves. The simulated process maytest one or more potential ORI configurations and identifies an improvedset of device-programmable ORI parameters to decrease the“false-positive burden” at the ICM. Optionally, the improved ORIprogramming parameters may be automatically pushed back to the ICM.Optionally, the templates created at 986 in FIG. 9E may be utilized toallow embodiments herein to accurately discriminate amongst sinusrhythm, different PVC morphologies, and various forms of (intermittent)block. Applied more broadly to a sufficiently long CA data set (up toand including the entirety of 24-hour monitoring), the R-wave detectionprocesses herein can provide detailed ventricular rhythm diagnostics.

The processes described herein afford an advantage in that the processesidentify a point on the QRS complex where the detection occurs in a moreconsistent manner beat-to-beat as compared to the conventional ORIprocess. Consistently identifying a detection point in the QRS complexis advantageous, given that R-wave timing is used for multiple reasons,such as a marker point for ensemble creation, for example, as withP-wave detection and ST-segment monitoring. In the event that thedetection point is misaligned on a QRS complex, the misalignment createsa challenge, particularly with wider QRS or split QRS, e.g., bundlebranch block. Misalignment of the detection point may also createdifficulties where R-waves are relatively small and T-waves arerelatively large, in which case T-wave detection (instead of R-wavedetection) provides reasonable intervals for rate-related diagnosticsbut obliterates possibility to use other electrogram features fordiscrimination and enhanced diagnostics.

VI. ALTERNATIVE EMBODIMENT—NOISE DETECTION ALGORITHM FOR CA SIGNALSSENSED BY IMPLANTABLE CARDIAC MONITOR (1^(ST) & 2^(ND) PASS)

ICMs are configured to evaluate various features of EGM signals todetermine specific cardiac events, such as P-waves, R-waves,arrhythmias, and the like. However, ICMs rely on relative clean EGMsignals to detect the features of interest. In certain instances, noisemay mimic certain features of interest from a cardiac event and thusgive rise to a false detection by the ICM, including but not limited tofalse device documented markers. The false detection may lead to aninappropriate therapy and/or diagnosis.

In accordance with embodiments herein, a noise detection process isprovided that identifies noise based on certain “turn” characteristicswithin a CA signal. As noted above, the term “turn” means a change in adirection of the CA signal. A turn may be further characterized by anamplitude, frequency and duration. High frequency noise is characterizedby very rapid changes in signal direction (as compared to changes insignal direction associated with sinus features of a cardiac event).Embodiments herein evaluate a presence (and characteristics) of highdensity, significant amplitude turns in a CA signal.

As explained hereafter, in accordance with embodiments herein, acomputer implemented method is provided for detecting arrhythmias incardiac activity. The method comprises, under control of one or moreprocessors configured with specific executable instructions, obtaining afar field cardiac activity (CA) data set that includes far field CAsignals for a series of beats; overlaying a segment of the CA signalswith a noise search window; identifying turns in the segment of the CAsignals; determining whether the turns exhibit a turn characteristicthat exceed a turn characteristic threshold; declaring the segment ofthe CA signals as a noise segment based on the determining operation;shifting the noise search window to a next segment of the CA signal andrepeat the analyzing and determining operations; and modifying the CAsignal based on the noise segments determined.

Additionally, or alternatively, the turn characteristic may correspondto turn amplitude and wherein the determining operation comprisesanalyzing the turn amplitude relative to a turn amplitude threshold.Additionally, or alternatively, the turn characteristic may correspondto turn frequency and wherein the determining operation comprisesanalyzing the turn frequency relative to a turn frequency threshold.Additionally, or alternatively, the method may further comprise settingnoise flags based on relations between the turn amplitude and turnamplitude threshold and between the turn frequency and turn frequencythreshold. Additionally, or alternatively, the method may furthercomprise applying an arrhythmia detection process that is dependent onRR interval variability, wherein the overlaying operation comprisesdefining the noise search window to overlap a portion of CA signal thatdoes not overlap with a QRS complex or a T-wave in the segment of the CAsignals. Additionally, or alternatively, the identifying the turn maycomprise identifying changes in a signal direction by calculating afirst derivate of the CA signals at incremental points along the CAsignals and finding the points where a sign of the derivative changes,labeling the points as turns.

In accordance with embodiments herein, a system is provided fordetecting arrhythmias in cardiac activity. The system comprises memoryto store specific executable instructions; and one or more processorsconfigured to execute the specific executable instructions for:obtaining a far field cardiac activity (CA) data set that includes farfield CA signals for a series of beats; overlaying a segment of the CAsignals with a noise search window; identifying turns in the segment ofthe CA signals; determining whether the turns exhibit a turncharacteristic that exceed a turn characteristic threshold; declaringthe segment of the CA signals as a noise segment based on thedetermining operation; shifting the noise search window to a nextsegment of the CA signal and repeat the analyzing and determiningoperations; and modifying the CA signal based on the noise segmentsdetermined.

Additionally, or alternatively, the characteristic may correspond toturn amplitude and wherein the determining operation comprises analyzingthe turn amplitude relative to a turn amplitude threshold. Additionally,or alternatively, the turn characteristic may correspond to turnfrequency and wherein the determining operation comprises analyzing theturn frequency relative to a turn frequency threshold. Additionally, oralternatively, the processor is further configured to set noise flagsbased on relations between a turn amplitude and turn amplitude thresholdand between a turn frequency and turn frequency threshold. Additionally,or alternatively, the processor is further configured to apply anarrhythmia detection process that is dependent on RR intervalvariability, wherein the overlaying operation comprises defining thenoise search window to overlap a portion of CA signal that does notoverlap with a QRS complex or a T-wave in the segment of the CA signals.Additionally, or alternatively, the processor is further configured toidentify changes in a signal direction by calculating a first derivateof the CA signals at incremental points along the CA signals and findthe points where a sign of the derivative changes, labeling the pointsas turns. Hereafter, embodiments of the foregoing method and system aredescribed.

FIG. 11 illustrates a process for identifying noise in accordance withembodiments herein. The operations of FIG. 11 may be performed as partof a second pass confirmation detection process (e.g., at 304 in FIG. 3). Additionally, or alternatively, the operations of FIG. 11 may beperformed as part of a first pass arrhythmia detection process, such asby an ICM, IMD, local external device, or remote server. At 1102, theone or more processors define and overlay a noise search window onto theCA signal for which noise detection is to be carried out. The noisesearch window may vary depending on a nature of the feature for whichthe CA signal is being analyzed. For example, the ICM may implement anORI process for AF detection, where the detection process is stronglydependent on the QRS morphology as the AF detection process is based onRR interval variability. Therefore, in an AF detection process that isdependent on RR interval variability, the noise detection process ofFIG. 11 defines the noise search window to overlap a portion of CAsignal that does not overlap with QRS complex to T-wave. For example,the processors may utilize QRS complex and/or T-wave markers that wereidentified prior to the process of FIG. 11 . As one example, R-wave andT-wave markers may be identified in real-time serially with the processof FIG. 11 . As another example, R-wave and T-wave markers may beidentified previously before the process of FIG. 11 (which may beperformed off-line or non-real-time).

At 1104, the one or more processors evaluate the CA signal within thenoise search window to identify turns in the CA signal. For example, theprocessors identify changes in the signal direction, (i.e., positive tonegative or negative to positive) by calculating a first derivate of theCA signal at incremental points along the CA signal and finding pointswhere a sign of the derivative changes from ‘positive’ to ‘negative’ or‘negative’ to ‘positive’. Each time the sign of the derivative changes,the processors label the point as a turn.

FIG. 12A illustrates an example of a CA signal segment 1202 (e.g., anEGM signal) that includes a QRS complex 1204 with an R-wave marker 1206.The process of FIG. 11 identifies the R-wave marker 1206 and defines anoise search window 1208 to precede the R-wave marker 1206 by apredetermined time interval. It should be recognized that the locationof the R-wave marker 1206 is merely an example, such as corresponding toan intermediate point within the R-wave. Optionally, the R-wave markermay correspond to the peak or other point of the R-wave. The processorsevaluate the CA signal in the noise search window 1208 by calculatingthe derivative at multiple points. The processors determine the pointswhere the derivative changes sign and labels each sign change as a turn.For example, FIG. 12A shows 4 ‘turns’ (indicated by black vertical lines1210-1213) marked by change in signal direction. Turns in the remainingportion of CA signal segment 1202 were not used as they fall within theQRS complex 1204 or thereafter in the T-wave portion of the CA signalsegment 1202.

Returning to FIG. 11 , at 1106-1114, the one or more processorsdetermine whether the turns exhibit turn characteristics that exceedturn characteristic thresholds and thus justify designating the currentCA signal segment within the search window to represent noise. At 1106,the one or more processors analyze a turn amplitude characteristicrelative to a turn amplitude threshold. The turn amplitude threshold setmay be such that turns having an amplitude that exceeds the turnamplitude threshold are considered “significant” turns in size relativeto a size of sinus features within a cardiac event. A “significant” turnrepresents a portion of the CA signal that could interfere with aspectsof the feature detection processes implemented herein. The turnamplitude threshold may be set in various manners, such as based on apercentage of a sinus feature. For example, the turn amplitude thresholdmay be set to equal 20% of an amplitude of a peak of an R-wave, 120% ofan amplitude of a peak of a P-wave or T-wave and the like. Optionally,the amplitude threshold may be set as a predetermined voltage and/or bysubtracting a predetermined voltage from an amplitude of the peak of anR-wave and/or adding a predetermined voltage to an amplitude of a peakof a P-wave or T-wave. At 1106, when the turn amplitude exceeds the turnamplitude threshold, the process determines that the turn has sufficientamplitude to potentially interfere with later analysis for detectingsinus features of interest, such as an R-wave. Thus, flow moves to 1110and sets an amplitude characteristic of interest (COI) candidate noiseflag. The amplitude COI candidate noise flag is set to indicate that thepotential exists that the current turn is sufficiently large (relativeto amplitudes of sinus features), such that, if the CA signal segment isnoise, the CA signal segment should be removed. At 1106, when the turnamplitude does not exceed the threshold, flow advances to 1108 and theamplitude COI candidate noise flag remains unset or at a zero/low value.

At 1108, the one or more processors analyze a turn frequencycharacteristic by comparing the turn frequency characteristic with aturn frequency threshold. The turn frequency characteristic representsthe number of turns (changes in direction) within the search windowand/or a desired portion of the search window. The turn frequencythreshold may be pre-programmed by a user and/or automatically setthroughout operation based on prior CA signals. For example, the turnfrequency threshold may be set to 5 Hz, 10 Hz and the like, where a turnfrequency characteristic of a current search window should exceed the 5Hz, 10 Hz or other threshold before being considered to have asufficiently high frequency to be indicative of noise. Optionally, theturn frequency threshold may be set to be a percentage of the heartrate, RR interval and the like. When the turn frequency characteristicexceeds the turn frequency threshold, flow moves from 1108 to 1112. At1112, the one or more processors set a frequency COI candidate noiseflag. The frequency COI candidate noise flag indicates that the turnsoccur with sufficient frequency to be indicative of noise. At 1108, whenthe turn frequency does not exceed the turn frequency threshold, flowadvances to 1114 and the frequency COI candidate noise flag remainsunset or at a zero/low value.

Optionally, the noise determination at 1106-1112 may be modified toutilize amplitude alone, frequency alone, or combination of both. Forexample, in embodiments that utilize amplitude alone, the operations at1108, 1112 may be omitted. In embodiments that utilize frequency alone,the operations at 1106, 1110 may be omitted.

At 1114, the one or more processors review the amplitude and/orfrequency COI candidate noise flags to determine whether the flagsindicate that the CA signal segment should be classified as noise. Inthe present example, the flags may represent binary values, namely setor unset. Additionally, or alternatively, at 1110 and 1112, the flagsmay record information representative of a degree to which the turnamplitude and/or turn frequency characteristics exceed the correspondingturn amplitude and frequency threshold. For example, when the turnamplitude characteristic is relatively close to the turn amplitudethreshold, the operation at 1110 may record a small value (e.g., 2-4 ona scale of 1-10). Alternatively, when the turn amplitude characteristicsubstantially exceeds the turn amplitude threshold, the operation at1110 may record a larger value (e.g., 7-9 on a scale of 1-10).Similarly, at 1112, the process may set the values for the frequency COIcandidate noise flag on a scale of 1-10 to be representative of a degreeto which the turn frequency characteristic exceeds the turn frequencythreshold.

At 1114, when the amplitude and frequency COI candidate noise flagsindicate that the CA signal segment includes excessive noise, flow movesto 1120. At 1114, when the noise flags do not indicate a presence ofnoise, the noise detection process of FIG. 11 ends for a current beat orCA signal segment. When the noise flags indicate a presence of noise,the process moves to 1120. At 1120, the one or more processors incrementa noisy beat counter by one. The noisy beat counter maintains a runningcount of the number of beats within a longer CA signal segment (e.g.,30-60 seconds) that satisfy one or both of the turn amplitude and turnfrequency criteria.

At 1122, the one or more processors compare a current count for thenoisy beat counter to a noise threshold. For example, the comparison maydetermine a percentage of noisy beats that were counted out of a totalnumber of beats. Alternatively, the noise threshold may merely representa predetermined number of beats. When the noisy beat count does notexceed the threshold, the process of FIG. 11 ends. Alternatively, whenthe noisy beat count exceeds the threshold, flow continues to 1124.

At 1124, the one or more processors register the current CA signalsegment as a noisy CA signal segment. By way of example, when a desirednumber X beats are classified as noisy out of a total number of beats Y,the processors register the current CA signal segment of Y beats asnoisy. For example, for a given CA signal segment, when a percentage (%)of noisy beats is greater than a threshold (e.g., 25%), then an overallentire CA signal segment (e.g., 30 second strip or 1 minute strip of EGMsignals for a CA data set) may be declared as ‘noisy’ and may berejected from further analysis. For example, when the operations of FIG.11 declare a 30 second EGM strip to represent a noisy segment, theoperation at 304 in FIG. 3 may reject the entire 30 second CA data setand return to 302 to obtain a new CA data set (e.g., a new 30 second EGMstrip).

Optionally, at 1124, a smaller portion of a CA data set may be rejectedand not an entire 30 second CA data set. For example, when a current CAsignal segment (e.g., 10-15 seconds of EGM signals or 10-20 beats) isdeemed to be noisy, only the individual noisy beats or the set ofnoising beats may be removed from the overall CA data set (e.g., removea 10-15 second CA signal segment from a 30-60 second CA signal segment).When noisy segments of a CA dataset are removed, the remaining segmentsof the CA data set represent noise-corrected CA signals. In the exampleof FIG. 3 , the noise-corrected CA signals may be passed from 304 to 306for further processing. Optionally, the noise detection process of FIG.11 may be performed as part of a first pass arrhythmia detectionprocess, where the noise detection operations are performed by firmwareand hardware within an ICM or IMD.

FIGS. 12B and 12C illustrate example CA signals 1222 and 1242,respectively, that are analyzed by the process of FIG. 11 in accordancewith embodiments herein. The CA signals 1222 and 1242 include QRScomplexes 1224 and 1244, respectively, with R-wave markers 1226 and1246. The process of FIG. 11 identifies the R-wave markers 1226 and 1246and define noise search windows 1228 and 1248 to precede the R-wavemarkers 1226 and by a predetermined time interval. The processorsdetermine the points where the derivatives change signs and label eachsign change as a turn. In FIG. 12B, 3 ‘turns’ (indicated by blackvertical lines 1230-1232) are marked by change in signal direction. InFIG. 12C, 4 ‘turns’ are indicated by vertical lines 1250-1253.

In FIG. 12B, the amplitudes of the turns at 1230-1232 are sufficientlylarge to exceed the turn amplitude threshold and thus would result inthe process of FIG. 11 setting the amplitude COI candidate noise flag at1110. Next, the process of FIG. 11 would analyze the frequency of theturns at 1230-1232 and determine that the frequency is sufficiently highto set the frequency COI candidate noise flag at 1112.

In FIG. 12C, the CA signal segment 1242 corresponds to an atrial fluttercardiac event. Regardless of whether the amplitudes of the turns at1250-1253 exceed an amplitude threshold, the frequency of the turns at1250-1253 is substantially lower than frequencies indicative of noise.Accordingly, the process of FIG. 11 would not classify the CA signalsegment 1242 as noise and instead would leave the CA signal segment 1242to be further analyzed for arrhythmia detection.

FIG. 12D illustrates strips of stored EGM signals (corresponding to CAsignals) collected by an ICM utilizing a conventional on-device noisedetection circuit. The EGM signal of FIG. 12D includes noise segmentswhich indicate that the noise segments did not trigger a noise mode atthe device noise detection circuit. Had the device noise detectioncircuit been triggered, the noise segments would have beenomitted/blanked. The EGM signals of FIG. 12D were characterized by theORI process on the ICM to include arrhythmia episodes. The ORI processidentified R-waves as indicated at the circular R-wave markers 1260.While the majority of the R-wave markers 1260 correctly identifyR-waves, some of the R-wave markers are incorrect. For example, theR-wave markers in CA signal segment 1262 and the R-wave marker in CAsignal segment 1264 represent false R-wave markers.

Also, in conventional R-wave detection processes, when the signalexhibits substantial noise, the noise may cause the location of theR-wave marker to be incorrectly positioned. Accordingly, while a CAsignal segment may include an R-wave, the R-wave detection process mayincorrectly position the R-wave marker within the QRS complex. Inaccurate positioning of R-wave markers may confine to otherdiscrimination operations that rely on the R-wave marker location, suchas in connection with setting search windows for other sinus andarrhythmia features of interest. As one example, when a discriminationoperation utilizes an ensemble average of P-waves in connection withsearching for a P-wave, the P-wave search windows will vary based on thelocation of the R-wave marker. Thus, incorrect positioning of the R-wavemarker may in turn lead to incorrect positioning of the P-wave searchwindows utilized to develop the ensemble average of the P-wave. Despitea presence of noise detection circuitry in conventional ICM, it has beenestimated that at least one conventional onboard AF detection processmay declare approximately 17% of false AF episodes as such due to noisethat is falsely detected as R-waves.

In accordance with embodiments herein, noisy signal segments may beidentified based on turns that have amplitude and frequencycharacteristics that exceed corresponding thresholds. The process ofFIG. 11 may be implemented in real time on an ICM contemporaneous withthe cardiac activity being analyzed. For example, the process of FIG. 11may be programmed to run within firmware on an ICM, while analyzing realtime EGM signals and/or analyzing EGM signals that have been temporarilystored in a spin buffer. For example, the process of FIG. 11 may beimplemented in an ICM as part of a confirmation process that analyzespre-recorded EGM signals (e.g., previously stored in a spin buffer). TheEGM signals may be stored in response to the ICM detecting an episodeutilizing a conventional arrhythmia detection process.

Additionally, or alternatively, the process of FIG. 11 may beimplemented non-real time, and applied as a retroactive or off-lineconfirmation process when analyzing CA data sets that include devicedocumented arrhythmias. If noise is found that would influence anarrhythmia detection algorithm, the CA signal segment having the noisemay be flagged or removed from the CA signal data set. Additionally, oralternatively, the process may record a notation in connection with theCA data set indicating that the device documented episode is based on aCA signal segment that exhibits substantial noise.

Additionally, or alternatively, the noise detection process of FIG. 11may be performed on an external instrument, such as a local mobiledevice (e.g., smart phone, tablet device, laptop computer) and/or aremote network server. When implemented at a local mobile device, thenoise detection process may be performed upon incoming CA data setsreceived by the local mobile device from an ICM before the local mobiledevice transmits the CA data set to the remote network server. Whenimplemented at a remote network server, the noise detection process maybe performed upon incoming CA data sets before displaying the CA dataset to a clinician. The noise detection process may flag, hide ordiscard CA signal segments that are classified as noisy segments by theprocess of FIG. 11 .

A model has been implemented based on an embodiment of the noisedetection process of FIG. 11 . The model analyzed ECG signals for 668 AFepisodes that were identified by an ICM to include AF (e.g., 668 devicedocumented AF episodes). From analysis of the 668 device documented AFepisodes by the model, 36 episodes (8.9%) were identified to exhibitexcess noise and were screened out by the noise detection process ofFIG. 11 . The 36 episodes identified by the present model were confirmedthrough trained observers to represent false positive AF episodes. Inaddition, the model did not filter out any true AF episodes. Inaccordance with embodiments herein, the noise detection process of FIG.11 may be implemented at an early point during and arrhythmia detectionprocess, such that CA signal segments that exhibit excess noise areremoved from further analysis by the arrhythmia detection process.

Embodiments may be implemented in connection with one or moreimplantable medical devices (IMDs). Non-limiting examples of IMDsinclude one or more of neurostimulator devices, implantable leadlessmonitoring and/or therapy devices, and/or alternative implantablemedical devices. For example, the IMD may represent a cardiac monitoringdevice, pacemaker, cardioverter, cardiac rhythm management device,defibrillator, neurostimulator, leadless monitoring device, leadlesspacemaker and the like. For example, the IMD may include one or morestructural and/or functional aspects of the device(s) described in U.S.Pat. No. 9,333,351 “Neurostimulation Method And System To Treat Apnea”and U.S. Pat. No. 9,044,610 “System And Methods For Providing ADistributed Virtual Stimulation Cathode For Use With An ImplantableNeurostimulation System”, which are hereby incorporated by reference.Additionally, or alternatively, the IMD may include one or morestructural and/or functional aspects of the device(s) described in U.S.Pat. No. 9,216,285 “Leadless Implantable Medical Device Having RemovableAnd Fixed Components” and U.S. Pat. No. 8,831,747 “LeadlessNeurostimulation Device And Method Including The Same”, which are herebyincorporated by reference. Additionally, or alternatively, the IMD mayinclude one or more structural and/or functional aspects of thedevice(s) described in U.S. Pat. No. 8,391,980 “Method And System ForIdentifying A Potential Lead Failure In An Implantable Medical Device”and U.S. Pat. No. 9,232,485 “System And Method For SelectivelyCommunicating With An Implantable Medical Device”, which are herebyincorporated by reference.

Closing

The various methods as illustrated in the Figures and described hereinrepresent exemplary embodiments of methods. The methods may beimplemented in software, hardware, or a combination thereof. In variousof the methods, the order of the steps may be changed, and variouselements may be added, reordered, combined, omitted, modified, etc.Various of the steps may be performed automatically (e.g., without beingdirectly prompted by user input) and/or programmatically (e.g.,according to program instructions).

Various modifications and changes may be made as would be obvious to aperson skilled in the art having the benefit of this disclosure. It isintended to embrace all such modifications and changes and, accordingly,the above description is to be regarded in an illustrative rather than arestrictive sense.

Various embodiments of the present disclosure utilize at least onenetwork that would be familiar to those skilled in the art forsupporting communications using any of a variety ofcommercially-available protocols, such as Transmission ControlProtocol/Internet Protocol (“TCP/IP”), User Datagram Protocol (“UDP”),protocols operating in various layers of the Open System Interconnection(“OSI”) model, File Transfer Protocol (“FTP”), Universal Plug and Play(“UpnP”), Network File System (“NFS”), Common Internet File System(“CIFS”) and AppleTalk. The network can be, for example, a local areanetwork, a wide-area network, a virtual private network, the Internet,an intranet, an extranet, a public switched telephone network, aninfrared network, a wireless network, a satellite network and anycombination thereof.

In embodiments utilizing a web server, the web server can run any of avariety of server or mid-tier applications, including Hypertext TransferProtocol (“HTTP”) servers, FTP servers, Common Gateway Interface (“CGI”)servers, data servers, Java servers, Apache servers and businessapplication servers. The server(s) also may be capable of executingprograms or scripts in response to requests from user devices, such asby executing one or more web applications that may be implemented as oneor more scripts or programs written in any programming language, such asJava®, C, C# or C++, or any scripting language, such as Ruby, PHP, Perl,Python or TCL, as well as combinations thereof. The server(s) may alsoinclude database servers, including without limitation thosecommercially available from Oracle®, Microsoft®, Sybase® and IBM® aswell as open-source servers such as MySQL, Postgres, SQLite, MongoDB,and any other server capable of storing, retrieving and accessingstructured or unstructured data. Database servers may includetable-based servers, document-based servers, unstructured servers,relational servers, non-relational servers or combinations of theseand/or other database servers.

The environment can include a variety of data stores and other memoryand storage media as discussed above. These can reside in a variety oflocations, such as on a storage medium local to (and/or resident in) oneor more of the computers or remote from any or all of the computersacross the network. In a particular set of embodiments, the informationmay reside in a storage-area network (“SAN”) familiar to those skilledin the art. Similarly, any necessary files for performing the functionsattributed to the computers, servers or other network devices may bestored locally and/or remotely, as appropriate. Where a system includescomputerized devices, each such device can include hardware elementsthat may be electrically coupled via a bus, the elements including, forexample, at least one central processing unit (“CPU” or “processor”), atleast one input device (e.g., a mouse, keyboard, controller, touchscreen or keypad) and at least one output device (e.g., a displaydevice, printer or speaker). Such a system may also include one or morestorage devices, such as disk drives, optical storage devices andsolid-state storage devices such as random access memory (“RAM”) orread-only memory (“ROM”), as well as removable media devices, memorycards, flash cards, etc.

Such devices also can include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device, etc.) and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a computer-readable storagemedium, representing remote, local, fixed and/or removable storagedevices as well as storage media for temporarily and/or more permanentlycontaining, storing, transmitting and retrieving computer-readableinformation. The system and various devices also typically will includea number of software applications, modules, services or other elementslocated within at least one working memory device, including anoperating system and application programs, such as a client applicationor web browser. It should be appreciated that alternate embodiments mayhave numerous variations from that described above. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets) or both. Further, connection to other computing devices suchas network input/output devices may be employed.

Various embodiments may further include receiving, sending, or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a computer-readable medium. Storage media and computerreadable media for containing code, or portions of code, can include anyappropriate media known or used in the art, including storage media andcommunication media, such as, but not limited to, volatile andnon-volatile, removable and non-removable media implemented in anymethod or technology for storage and/or transmission of information suchas computer readable instructions, data structures, program modules orother data, including RAM, ROM, Electrically Erasable ProgrammableRead-Only Memory (“EEPROM”), flash memory or other memory technology,Compact Disc Read-Only Memory (“CD-ROM”), digital versatile disk (DVD)or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices or any other medium whichcan be used to store the desired information and which can be accessedby the system device. Based on the disclosure and teachings providedherein, a person of ordinary skill in the art will appreciate other waysand/or methods to implement the various embodiments.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the invention asset forth in the claims.

Other variations are within the spirit of the present disclosure. Thus,while the disclosed techniques are susceptible to various modificationsand alternative constructions, certain illustrated embodiments thereofare shown in the drawings and have been described above in detail. Itshould be understood, however, that there is no intention to limit theinvention to the specific form or forms disclosed, but on the contrary,the intention is to cover all modifications, alternative constructionsand equivalents falling within the spirit and scope of the invention, asdefined in the appended claims.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosed embodiments (especially in thecontext of the following claims) are to be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The terms “comprising,” “having,” “including”and “containing” are to be construed as open-ended terms (i.e., meaning“including, but not limited to,”) unless otherwise noted. The term“connected,” when unmodified and referring to physical connections, isto be construed as partly or wholly contained within, attached to orjoined together, even if there is something intervening. Recitation ofranges of values herein are merely intended to serve as a shorthandmethod of referring individually to each separate value falling withinthe range, unless otherwise indicated herein and each separate value isincorporated into the specification as if it were individually recitedherein. The use of the term “set” (e.g., “a set of items”) or “subset”unless otherwise noted or contradicted by context, is to be construed asa nonempty collection comprising one or more members. Further, unlessotherwise noted or contradicted by context, the term “subset” of acorresponding set does not necessarily denote a proper subset of thecorresponding set, but the subset and the corresponding set may beequal.

Operations of processes described herein can be performed in anysuitable order unless otherwise indicated herein or otherwise clearlycontradicted by context. Processes described herein (or variationsand/or combinations thereof) may be performed under the control of oneor more computer systems configured with executable instructions and maybe implemented as code (e.g., executable instructions, one or morecomputer programs or one or more applications) executing collectively onone or more processors, by hardware or combinations thereof. The codemay be stored on a computer-readable storage medium, for example, in theform of a computer program comprising a plurality of instructionsexecutable by one or more processors. The computer-readable storagemedium may be non-transitory.

All references, including publications, patent applications and patents,cited herein are hereby incorporated by reference to the same extent asif each reference were individually and specifically indicated to beincorporated by reference and were set forth in its entirety herein.

It is to be understood that the subject matter described herein is notlimited in its application to the details of construction and thearrangement of components set forth in the description herein orillustrated in the drawings hereof. The subject matter described hereinis capable of other embodiments and of being practiced or of beingcarried out in various ways. Also, it is to be understood that thephraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. The use of“including,” “comprising,” or “having” and variations thereof herein ismeant to encompass the items listed thereafter and equivalents thereofas well as additional items.

It is to be understood that the above description is intended to beillustrative, and not restrictive. For example, the above-describedembodiments (and/or aspects thereof) may be used in combination witheach other. In addition, many modifications may be made to adapt aparticular situation or material to the teachings of the inventionwithout departing from its scope. While the dimensions, types ofmaterials and physical characteristics described herein are intended todefine the parameters of the invention, they are by no means limitingand are exemplary embodiments. Many other embodiments will be apparentto those of skill in the art upon reviewing the above description. Thescope of the invention should, therefore, be determined with referenceto the appended claims, along with the full scope of equivalents towhich such claims are entitled. In the appended claims, the terms“including” and “in which” are used as the plain-English equivalents ofthe respective terms “comprising” and “wherein.” Moreover, in thefollowing claims, the terms “first,” “second,” and “third,” etc. areused merely as labels, and are not intended to impose numericalrequirements on their objects. Further, the limitations of the followingclaims are not written in means—plus-function format and are notintended to be interpreted based on 35 U.S.C. § 112(f), unless and untilsuch claim limitations expressly use the phrase “means for” followed bya statement of function void of further structure.

What is claimed is:
 1. A computer implemented method for detecting noisein cardiac activity, comprising: under control of one or more processorsconfigured with specific executable instructions, obtaining a far fieldcardiac activity (CA) data set that includes far field CA signals for aseries of beats; overlaying a segment of the CA signals with a noisesearch window; identifying turns in the segment of the CA signals;determining whether the turns exhibit a turn characteristic that exceeda turn characteristic threshold; declaring the segment of the CA signalsas a noise segment based on the determining operation; shifting thenoise search window to a next segment of the CA signal and repeat theidentifying, determining and declaring operations; and modifying the CAsignals based on the declaring the noise segments.
 2. The method ofclaim 1, wherein the turn characteristic corresponds to turn amplitudeand wherein the determining operation comprises analyzing the turnamplitude relative to a turn amplitude threshold.
 3. The method of claim2, wherein the turn characteristic corresponds to turn frequency andwherein the determining operation comprises analyzing the turn frequencyrelative to a turn frequency threshold.
 4. The method of claim 3,further comprising setting noise flags based on relations between theturn amplitude and turn amplitude threshold and between the turnfrequency and turn frequency threshold.
 5. The method of claim 1,wherein the identifying the turn comprises identifying changes in asignal direction by calculating a first derivate of the CA signals atincremental points along the CA signals and finding the points where asign of the derivative changes, labeling the points as turns.
 6. Themethod of claim 1, wherein the overlaying operation comprises definingthe noise search window to overlap a portion of CA signal that does notoverlap with a QRS complex or a T-wave in the segment of the CA signals.7. The method of claim 1, further comprising applying an arrhythmiadetection process, based on RR interval variability, to the CA signalsas modified.
 8. The method of claim 7, further comprising declaring asegment of the CA signals to represent a noisy segment, the modifyingoperation comprising removing the noisy segment to form noise correctedCA signals, the applying operation applying the arrhythmia detectionprocess to the noise corrected CA signals.
 9. The method of claim 7,wherein the arrhythmia detection process is performed as a first passdetection process by an on-board R-R interval irregularity (ORI) processthat analyzes the CA signals after being modified based on the declaringthe noisy segments.
 10. The method of claim 1, wherein the overlaying,identifying, determining, declaring, shifting, modifying operations areperformed by firmware and hardware within an ICM or IMD.
 11. A systemfor detecting noise in cardiac activity, comprising: memory to storespecific executable instructions; one or more processors configured toexecute the specific executable instructions for: obtaining a far fieldcardiac activity (CA) data set that includes far field CA signals for aseries of beats; overlaying a segment of the CA signals with a noisesearch window; identifying turns in the segment of the CA signals;determining whether the turns exhibit a turn characteristic that exceeda turn characteristic threshold; declaring the segment of the CA signalsas a noise segment based on the determining operation; shifting thenoise search window to a next segment of the CA signal and repeat theidentifying, determining and declaring operations; and modifying the CAsignals based on the declaring the noise segments.
 12. The system ofclaim 11, wherein the characteristic corresponds to turn amplitude andwherein the determining operation comprises analyzing the turn amplituderelative to a turn amplitude threshold.
 13. The system of claim 12,wherein the turn characteristic corresponds to turn frequency andwherein the determining operation comprises analyzing the turn frequencyrelative to a turn frequency threshold.
 14. The system of claim 11,wherein the processor is further configured to set noise flags based onrelations between a turn amplitude and turn amplitude threshold andbetween a turn frequency and turn frequency threshold.
 15. The system ofclaim 11, wherein the processor is further configured to apply anarrhythmia detection process that is dependent on RR intervalvariability, wherein the overlaying operation comprises defining thenoise search window to overlap a portion of CA signal that does notoverlap with a QRS complex or a T-wave in the segment of the CA signals.16. The system of claim 11, wherein the processor is further configuredto identify changes in a signal direction by calculating a firstderivate of the CA signals at incremental points along the CA signalsand find the points where a sign of the derivative changes, labeling thepoints as turns.
 17. The system of claim 11, further comprising animplantable cardiac monitor that houses the memory and one or moreprocessors, and that houses sensors to obtain the CA signals for theseries of beats.
 18. The system of claim 11, further comprising animplantable cardiac monitor that comprises sensors to obtain the CAsignals and a telemetry circuit to transmit the CA signals to a localexternal device.
 19. The system of claim 18, further comprising a localexternal device that includes the memory and one or more processors forperforming at least a portion of the overlaying, identifying,determining, declaring, shifting, and modifying operations.
 20. Thesystem of claim 18, further comprising a remote server that includes thememory and one or more processors for performing at least a portion ofthe overlaying, identifying, determining, declaring, shifting, andmodifying operations.